CN117514431A - DPF fault diagnosis method, device, terminal equipment and storage medium - Google Patents
DPF fault diagnosis method, device, terminal equipment and storage medium Download PDFInfo
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Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
- F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
- F01N11/00—Monitoring or diagnostic devices for exhaust-gas treatment apparatus, e.g. for catalytic activity
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
- F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
- F01N3/00—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust
- F01N3/02—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for cooling, or for removing solid constituents of, exhaust
- F01N3/021—Exhaust or silencing apparatus having means for purifying, rendering innocuous, or otherwise treating exhaust for cooling, or for removing solid constituents of, exhaust by means of filters
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
- F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
- F01N2900/00—Details of electrical control or of the monitoring of the exhaust gas treating apparatus
- F01N2900/06—Parameters used for exhaust control or diagnosing
- F01N2900/12—Parameters used for exhaust control or diagnosing said parameters being related to the vehicle exterior
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
- F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
- F01N2900/00—Details of electrical control or of the monitoring of the exhaust gas treating apparatus
- F01N2900/06—Parameters used for exhaust control or diagnosing
- F01N2900/14—Parameters used for exhaust control or diagnosing said parameters being related to the exhaust gas
- F01N2900/1404—Exhaust gas temperature
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
- F01N—GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR MACHINES OR ENGINES IN GENERAL; GAS-FLOW SILENCERS OR EXHAUST APPARATUS FOR INTERNAL COMBUSTION ENGINES
- F01N2900/00—Details of electrical control or of the monitoring of the exhaust gas treating apparatus
- F01N2900/06—Parameters used for exhaust control or diagnosing
- F01N2900/14—Parameters used for exhaust control or diagnosing said parameters being related to the exhaust gas
- F01N2900/1406—Exhaust gas pressure
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
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- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
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Abstract
The invention discloses a DPF fault diagnosis method, a device, terminal equipment and a storage medium.
Description
Technical Field
The present invention relates to the field of pollutant emission control, and in particular, to a DPF fault diagnosis method, device, terminal equipment, and storage medium.
Background
The DPF (Diesel Particulate Filter ) is used as part of the aftertreatment device, so that the emission of the diesel vehicle can be effectively controlled. Since the DPF is installed in an engine exhaust system, the exhaust gas temperature is high and the working environment is bad, so it becomes particularly important to ensure the reliability of its operation. Emission regulations clearly require that the operating conditions of the DPF device must be monitored in real time. In the DPF use process, the problems of improper regeneration control, vibration, thermal shock and the like are easy to cause the failure of the post-treatment device, and if the failure of the post-treatment device cannot be found and treated as early as possible, the power performance and the fuel economy of an automobile are affected, and even the exhaust emission exceeds the regulation limit value and pollutes the atmosphere. Therefore, the DPF fault diagnosis is carried out by adopting a proper technology, and the DPF fault diagnosis method is a powerful guarantee that the whole DPF can stably run.
Aiming at the DPF performance diagnosis method of the diesel engine, related enterprises at home and abroad at present mainly diagnose through a diagnosis model based on carbon loading. However, the conventional method for performing performance diagnosis by means of an accurate model requires a large number of matching experiments for the diesel engine and the post-processing device because of various types of diesel engine models, wide power section range of the diesel engine, various service lives and maintenance conditions of the vehicle, and different shapes of the DPF due to limited installation positions and spaces of the diesel engine. With the development of remote monitoring and big data technology, the remote performance diagnosis technology based on data driving has the advantages of high fault recognition accuracy, no need of establishing an accurate mathematical model and the like. However, the current remote performance diagnosis technology mainly stays in a single mode of identifying according to the front and rear pressure drop threshold values of the DPF, and cannot accurately and effectively judge the running state of the DPF, so that the fault of the DPF cannot be accurately diagnosed.
Therefore, a solution for accurately and effectively judging the operation state of the DPF is needed.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide a DPF fault diagnosis method, a device, terminal equipment and a storage medium, which aim to accurately and effectively judge the DPF running state so as to accurately diagnose the DPF fault.
In order to achieve the above object, the present invention provides a DPF failure diagnosis method including:
and determining the operating state of the DPF based on the target operating condition data of the DPF when the DPF is in a non-dismantling state.
Optionally, before the step of determining the operation state of the DPF based on the target operation state data of the DPF in the non-dismantling state of the DPF, the method further includes:
acquiring target operation condition data of the DPF; and/or the number of the groups of groups,
determining whether the DPF is in a non-removed state based on the target operating condition data.
Optionally, the step of obtaining the target operating condition data of the DPF includes:
acquiring original operation condition data of the DPF through a preset sensor;
and processing and analyzing the original operation condition data to obtain the target operation condition data.
Optionally, the original operation condition data includes feature data corresponding to each sampling point, and the step of processing and analyzing the original operation condition data includes:
Identifying invalid data, missing data and abnormal data in the characteristic data;
filtering the characteristic data corresponding to each sampling point according to the invalid data, and repairing the invalid data and/or the missing data by a linear interpolation method;
and correcting the abnormal data by a time sequence standard smoothing algorithm.
Optionally, the characteristic data includes at least one of position data, vehicle speed data, pressure drop data, temperature data, and exhaust flow rate, and the invalid data in the characteristic data includes at least one of:
the speed data with the speed smaller than a first preset speed threshold value when the position data changes or disappears;
vehicle speed data exceeding a second preset vehicle speed threshold;
the vehicle speed data with the difference value between the vehicle speed data and the adjacent data exceeding the preset vehicle speed difference value in the preset acquisition time;
vehicle speed data below a third vehicle speed threshold and having a duration exceeding a preset first duration;
pressure drop data outside of the effective pressure drop threshold range;
continuously exceeding a preset number of sampling points to keep unchanged pressure drop data;
pressure drop data with the difference value between the adjacent data exceeding a preset pressure drop value;
Temperature data outside of an effective temperature threshold range;
temperature data of 0 in a vehicle start state;
temperature data maintained at the same value for more than a second preset duration;
temperature data with the difference value exceeding the preset temperature difference value with the adjacent data.
Optionally, the step of filtering the feature data corresponding to each sampling point according to the invalid data and repairing the invalid data and/or the missing data by a linear interpolation method includes:
selecting or determining a sampling point corresponding to the invalid data as an invalid sampling point;
deleting the characteristic data corresponding to the invalid sampling points;
determining adjacent sampling points based on the invalid sampling points and/or sampling points corresponding to the missing data;
and carrying out linear calculation according to the adjacent characteristic data corresponding to the adjacent sampling points to obtain linear difference data for repairing the invalid data and/or the missing data.
Optionally, the step of determining whether the DPF is in a non-removed state based on the target operating condition data includes:
judging whether pressure drop data which is smaller than a preset pressure drop threshold value and the duration exceeds a preset time threshold value exists in the target operation condition data;
If the pressure drop data which is smaller than a preset pressure drop threshold value and the duration exceeds a preset time threshold value exists in the target operation condition data, judging that the DPF is in a dismantling state;
and if no pressure drop data which is smaller than a preset pressure drop threshold value and the duration exceeds a preset time threshold value exists in the target operation condition data, judging that the DPF is in a non-dismantling state.
Optionally, the target operating condition data includes pressure drop data and/or temperature data after analysis, and the step of determining the operating state of the DPF based on the target operating condition data of the DPF includes:
determining the running state of the DPF according to the pressure drop data after analysis and processing based on a preset matching relation; and/or the number of the groups of groups,
and performing linear fitting on the analyzed and processed temperature data to obtain a fitting result, and determining the running state of the DPF according to the fitting result.
Optionally, the step of determining the operation state of the DPF according to the pressure drop data after the analysis and processing based on the preset matching relationship further includes:
determining the DPF blocking degree of a first preset step length and each pressure drop value corresponding to the exhaust flow of a second preset step length through preset model software;
And establishing the preset matching relation according to each pressure drop value.
The step of determining the operation state of the DPF according to the pressure drop data after the analysis processing based on the preset matching relationship includes:
selecting or determining the exhaust flow corresponding to the pressure drop data after analysis and treatment;
comparing the analyzed pressure drop data and the corresponding exhaust flow with the preset matching relation to determine the blocking degree of the target DPF;
if the target DPF blocking degree is in a first degree interval, judging that the DPF is in a damaged state;
if the target DPF blocking degree is in a second degree interval, judging that the DPF is in a normal state;
and if the target DPF clogging degree is in a third degree interval, judging that the DPF is in a clogging state.
Optionally, the step of linearly fitting the pressure drop data and/or the temperature data after the analysis processing to obtain a fitting result includes:
calculating a plurality of groups of temperature difference accumulation addition values according to the analyzed and processed temperature data;
and performing linear fitting on the plurality of groups of temperature difference accumulation added values according to a least square linear regression method to obtain a fitting result.
Optionally, the step of linearly fitting the plurality of groups of temperature difference accumulation added values according to a least square method linear regression method to obtain the fitting result further includes:
calculating to obtain a first fitting curve slope average value and a first deviation according to fitting results corresponding to a first typical working condition of preset times;
judging whether the first deviation exceeds a preset deviation range or not;
if the first deviation exceeds a preset deviation range, selecting a fitting result corresponding to a second typical working condition of preset times, and calculating to obtain a second fitting curve slope average value and a second deviation;
and if the second deviation exceeds a preset deviation range, judging that the fitting result does not meet a preset judging requirement.
Optionally, the step of determining the operation state of the DPF according to the fitting result includes:
selecting or determining the slope of a fitting curve from fitting results meeting the preset judging requirements;
comparing the slope of the fitted curve with a preset threshold, wherein the preset threshold comprises a first slope threshold and a second slope threshold;
if the slope of the fitted curve is larger than the first slope threshold, judging that the DPF is in a damaged state;
If the slope of the fitted curve is between the first slope threshold and the second slope threshold, judging that the DPF is in a normal state;
and if the slope of the fitted curve is smaller than the second slope threshold value, judging that the DPF is in a blocking state.
In addition, in order to achieve the above object, the present invention also provides a DPF failure diagnosis apparatus including:
and the identification module is used for determining the running state of the DPF based on the target running condition data of the DPF when the DPF of the diesel vehicle particulate filter is in a non-dismantling state.
In addition, in order to achieve the above object, the present invention also provides a terminal device including a memory, a processor, and a DPF failure diagnosis program stored on the memory and operable on the processor, which when executed by the processor, implements the steps of the DPF failure diagnosis method as described above.
In addition, in order to achieve the above object, the present invention also provides a computer-readable storage medium having stored thereon a DPF failure diagnosis program which, when executed by a processor, implements the steps of the DPF failure diagnosis method as described above.
According to the DPF fault diagnosis method, device, terminal equipment and storage medium provided by the embodiment of the invention, the running state of the DPF is determined based on the target running condition data of the DPF when the DPF is in a non-dismantling state, so that the running state of the DPF is monitored in real time, the DPF fault can be diagnosed accurately in time, and corresponding treatment measures are taken, so that the stable running of the DPF can be ensured.
Drawings
FIG. 1 is a schematic diagram of functional modules of a terminal device to which a DPF failure diagnosis apparatus of the present invention belongs;
FIG. 2 is a flow chart of an exemplary embodiment of a DPF failure diagnosis method of the present invention;
FIG. 3 is a flowchart illustrating the acquisition of target operating condition data of the DPF according to the embodiment of FIG. 2;
FIG. 4 is a schematic diagram of a DPF system in accordance with an embodiment of the invention;
FIG. 5 is a schematic diagram of a data processing flow in an embodiment of the invention;
FIG. 6 is a schematic illustration of a specific flow chart for determining the operating state of the DPF based on the target operating condition data of the DPF in the embodiment of FIG. 2;
FIG. 7 is a schematic diagram of DPF pressure drop in an embodiment of the invention;
FIG. 8 is an exemplary MAP diagram provided in an embodiment of the present invention;
FIG. 9 is a graph showing an example of the slope of a fitted curve in an embodiment of the present invention;
FIG. 10 is a diagram showing the actual measurement results in the embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The main solutions of the embodiments of the present invention are: through being in under the non-state of demolishing at diesel vehicle particulate matter trap DPF, confirm based on DPF's object operating condition data DPF operating condition's real-time supervision has been realized DPF operating condition to in time accurately diagnose DPF's trouble, and then take corresponding processing measure, thereby can ensure DPF's steady operation.
Technical terms related to the embodiment of the invention:
DPF (Diesel Particulate Filter ).
DPFs have been widely used as the most effective control device for reducing the emission of diesel particulate matter. Since the DPF is installed in an engine exhaust system, the exhaust gas temperature is high and the working environment is bad, so it becomes particularly important to ensure the reliability of its operation. Emission regulations clearly require that the operating conditions of the DPF device must be monitored in real time. In the DPF use process, the problems of improper regeneration control, vibration, thermal shock and the like are easy to cause the failure of the post-treatment device, and if the failure of the post-treatment device cannot be found and treated as early as possible, the power performance and the fuel economy of an automobile are affected, and even the exhaust emission exceeds the regulation limit value and pollutes the atmosphere. Therefore, fault diagnosis is performed by adopting a proper technology, and the method is a powerful guarantee that the whole DPF can stably run.
Aiming at the performance diagnosis method of the DPF post-treatment device of the diesel engine, related enterprises at home and abroad at present mainly diagnose by a diagnosis model based on carbon loading. However, the conventional method for performing performance diagnosis by means of an accurate model requires a large number of matching experiments for the diesel engine and the post-processing device because of various types of diesel engine models, wide power section range of the diesel engine, various service lives and maintenance conditions of the vehicle, and different shapes of the DPF due to limited installation positions and spaces of the diesel engine. With the development of remote monitoring and big data technology, the data-driven performance diagnosis technology has the advantages of high fault identification accuracy, no need of establishing an accurate mathematical model and the like. However, the current remote monitoring data is mainly used for identification based on the pressure drop threshold before and after the DPF, and the DPF running state cannot be accurately and effectively judged. How to accurately judge DPF faults and establish unified standards by utilizing real-time running state detection data of a mass post-processing device obtained by a remote monitoring technology is becoming a difficult problem to be solved in DPF production units and the inside and outside of the automobile industry.
The invention provides a solution, by determining the running state of the DPF based on the target running condition data of the DPF in a non-dismantling state of the DPF, the running state of the DPF is monitored in real time, so that the fault of the DPF can be accurately diagnosed in time, and corresponding treatment measures are taken, so that the stable running of the DPF can be ensured.
Specifically, referring to fig. 1, fig. 1 is a schematic diagram of functional modules of a terminal device to which the DPF failure diagnosis apparatus of the present invention belongs. The DPF failure diagnosis apparatus may be an apparatus capable of performing DPF failure diagnosis independent of the terminal device, which may be carried on the terminal device in the form of hardware or software. The terminal equipment can be an intelligent mobile terminal with a data processing function such as a mobile phone and a tablet personal computer, and can also be a fixed terminal equipment or a server with a data processing function.
In this embodiment, the terminal device to which the DPF fault diagnosis apparatus belongs includes at least an output module 110, a processor 120, a memory 130, and a communication module 140.
The memory 130 stores an operating system and a DPF failure diagnosis program, and the DPF failure diagnosis apparatus may store information such as target operation condition data of the DPF in the memory 130; the output module 110 may be a display screen or the like. The communication module 140 may include a WIFI module, a mobile communication module, a bluetooth module, and the like, and communicates with an external device or a server through the communication module 140.
Wherein the DPF fault diagnosis program in the memory 130, when executed by the processor, implements the steps of:
And determining the operating state of the DPF based on the target operating condition data of the DPF when the DPF is in a non-dismantling state.
Further, the DPF fault diagnosis program in the memory 130, when executed by the processor, also implements the steps of:
acquiring target operation condition data of the DPF; and/or the number of the groups of groups,
determining whether the DPF is in a non-removed state based on the target operating condition data.
Further, the DPF fault diagnosis program in the memory 130, when executed by the processor, also implements the steps of:
acquiring original operation condition data of the DPF through a preset sensor;
and processing and analyzing the original operation condition data to obtain the target operation condition data.
Further, the DPF fault diagnosis program in the memory 130, when executed by the processor, also implements the steps of:
identifying invalid data, missing data and abnormal data in the characteristic data;
filtering the characteristic data corresponding to each sampling point according to the invalid data, and repairing the invalid data and/or the missing data by a linear interpolation method;
and correcting the abnormal data by a time sequence standard smoothing algorithm.
Further, the DPF fault diagnosis program in the memory 130, when executed by the processor, also implements the steps of:
Selecting or determining a sampling point corresponding to the invalid data as an invalid sampling point;
deleting the characteristic data corresponding to the invalid sampling points;
determining adjacent sampling points based on the invalid sampling points and/or sampling points corresponding to the missing data;
and carrying out linear calculation according to the adjacent characteristic data corresponding to the adjacent sampling points to obtain linear difference data for repairing the invalid data and/or the missing data.
Further, the DPF fault diagnosis program in the memory 130, when executed by the processor, also implements the steps of:
judging whether pressure drop data which is smaller than a preset pressure drop threshold value and the duration exceeds a preset time threshold value exists in the target operation condition data;
if the pressure drop data which is smaller than a preset pressure drop threshold value and the duration exceeds a preset time threshold value exists in the target operation condition data, judging that the DPF is in a dismantling state;
and if no pressure drop data which is smaller than a preset pressure drop threshold value and the duration exceeds a preset time threshold value exists in the target operation condition data, judging that the DPF is in a non-dismantling state.
Further, the DPF fault diagnosis program in the memory 130, when executed by the processor, also implements the steps of:
Determining the running state of the DPF according to the pressure drop data after analysis and processing based on a preset matching relation; and/or the number of the groups of groups,
and performing linear fitting on the analyzed and processed temperature data to obtain a fitting result, and determining the running state of the DPF according to the fitting result.
Further, the DPF fault diagnosis program in the memory 130, when executed by the processor, also implements the steps of:
determining the DPF blocking degree of a first preset step length and each pressure drop value corresponding to the exhaust flow of a second preset step length through preset model software;
and establishing the preset matching relation according to each pressure drop value.
Further, the DPF fault diagnosis program in the memory 130, when executed by the processor, also implements the steps of:
selecting or determining the exhaust flow corresponding to the pressure drop data after analysis and treatment;
comparing the analyzed pressure drop data and the corresponding exhaust flow with the preset matching relation to determine the blocking degree of the target DPF;
if the target DPF blocking degree is in a first degree interval, judging that the DPF is in a damaged state;
if the target DPF blocking degree is in a second degree interval, judging that the DPF is in a normal state;
And if the target DPF clogging degree is in a third degree interval, judging that the DPF is in a clogging state.
Further, the DPF fault diagnosis program in the memory 130, when executed by the processor, also implements the steps of:
calculating a plurality of groups of temperature difference accumulation addition values according to the analyzed and processed temperature data;
and performing linear fitting on the plurality of groups of temperature difference accumulation added values according to a least square linear regression method to obtain a fitting result.
Further, the DPF fault diagnosis program in the memory 130, when executed by the processor, also implements the steps of:
the step of linearly fitting the plurality of groups of temperature difference accumulation added values according to a least square method linear regression method to obtain the fitting result further comprises the following steps:
calculating to obtain a first fitting curve slope average value and a first deviation according to fitting results corresponding to a first typical working condition of preset times;
judging whether the first deviation exceeds a preset deviation range or not;
if the first deviation exceeds a preset deviation range, selecting a fitting result corresponding to a second typical working condition of preset times, and calculating to obtain a second fitting curve slope average value and a second deviation;
and if the second deviation exceeds a preset deviation range, judging that the fitting result does not meet a preset judging requirement.
Further, the DPF fault diagnosis program in the memory 130, when executed by the processor, also implements the steps of:
selecting or determining the slope of a fitting curve from fitting results meeting the preset judging requirements;
comparing the slope of the fitted curve with a preset threshold, wherein the preset threshold comprises a first slope threshold and a second slope threshold;
if the slope of the fitted curve is larger than the first slope threshold, judging that the DPF is in a damaged state;
if the slope of the fitted curve is between the first slope threshold and the second slope threshold, judging that the DPF is in a normal state;
and if the slope of the fitted curve is smaller than the second slope threshold value, judging that the DPF is in a blocking state.
According to the technical scheme, the DPF is particularly in the non-dismantling state through the diesel vehicle particulate matter trap, the running state of the DPF is determined based on the target running condition data of the DPF, the running state of the DPF is monitored in real time, so that the fault of the DPF can be diagnosed accurately in time, and corresponding treatment measures are taken, so that the stable running of the DPF can be ensured.
The method embodiment of the invention is proposed based on the above-mentioned terminal equipment architecture but not limited to the above-mentioned architecture.
The main execution body of the method of the present embodiment may be a DPF failure diagnosis apparatus or a terminal device, etc., and the present embodiment is exemplified by the DPF failure diagnosis apparatus.
Referring to fig. 2, fig. 2 is a flowchart illustrating an exemplary embodiment of a DPF failure diagnosis method according to the present invention. The DPF fault diagnosis method comprises the following steps:
step S10, determining the running state of the DPF based on the target running condition data of the DPF under the condition that the DPF of the diesel vehicle is in a non-dismantling state.
Specifically, since the DPF is usually installed in an engine exhaust system, due to the influence of environmental factors such as high exhaust gas temperature, vibration, thermal shock, etc., the DPF is liable to fail, if the failure of the DPF cannot be diagnosed and treated as soon as possible, not only the dynamic performance and fuel economy of an automobile are affected, but also the exhaust gas emission is even caused to exceed the regulatory limit value, and the atmospheric environment is polluted.
The embodiment of the invention provides a method for accurately and effectively judging the running state of a DPF, which is based on target running condition data of the DPF, and is used for identifying the running state of the DPF in a non-dismantling state so as to realize real-time monitoring of the running state of the DPF.
Optionally, before the step of determining the operation state of the DPF based on the target operation state data of the DPF in the non-dismantling state of the DPF, the method further includes:
Acquiring target operation condition data of the DPF; and/or the number of the groups of groups,
determining whether the DPF is in a non-removed state based on the target operating condition data.
Optionally, in the embodiment of the invention, a temperature sensor and a pressure sensor CAN be additionally arranged at the inlet end and the outlet end of the DPF, and the temperature sensor and the pressure sensor are connected with a DPF controller and are in communication with a CAN protocol. The DPF controller is connected with the GPS module, original operation condition data of the DPF can be acquired through each sensor, and target operation condition data of the DPF can be obtained through processing and analyzing the original operation condition data.
Optionally, the target operating condition data includes analysis processed temperature data.
Optionally, the step of determining whether the DPF is in a non-removed state based on the target operating condition data includes:
judging whether pressure drop data which is smaller than a preset pressure drop threshold value and the duration exceeds a preset time threshold value exists in the target operation condition data;
if the pressure drop data which is smaller than a preset pressure drop threshold value and the duration exceeds a preset time threshold value exists in the target operation condition data, judging that the DPF is in a dismantling state;
and if no pressure drop data which is smaller than a preset pressure drop threshold value and the duration exceeds a preset time threshold value exists in the target operation condition data, judging that the DPF is in a non-dismantling state.
Optionally, before performing fault judgment, diagnosing whether the DPF device is removed, where the preset pressure drop threshold in the embodiment of the present invention may be 0.1kpa, and the preset time threshold may be 10 minutes, for example, if the pressure drop value is suddenly less than 0.1kpa and remains unchanged for more than 10 minutes on the premise that the differential pressure sensor data meets the standard quality requirement, the DPF may be considered to be removed.
Optionally, after confirming the non-dismantling state of the DPF, further judging whether the DPF is blocked or damaged by utilizing the slope change of the straight line fitted by the curve of the accumulated value of the temperature difference before and after the DPF.
Alternatively, the fitting result may be obtained by performing linear fitting on the analyzed and processed temperature data, for determining the operation state of the DPF, wherein the operation state of the DPF includes a broken state, a normal state, and a clogged state.
In this embodiment, by determining the running state of the DPF based on the target running condition data of the DPF when the diesel vehicle particulate filter DPF is in a non-dismantling state, real-time monitoring of the running state of the DPF is achieved, so that the fault of the DPF can be accurately diagnosed in time, and corresponding treatment measures can be taken, so that stable running of the DPF can be ensured.
Referring to fig. 3, fig. 3 is a schematic flow chart of acquiring target operation condition data of the DPF in the embodiment of fig. 2. The present embodiment is based on the embodiment shown in fig. 2, and in this embodiment, the step of obtaining the target operation condition data of the DPF includes:
step S001, acquiring original operation condition data of the DPF through a preset sensor;
optionally, the DPF fault diagnosis method provided in the embodiment of the present invention mainly adopts the uploaded DPF related operation condition monitoring data to judge the typical fault of the DPF when the heavy diesel vehicle is running on the actual road, including links such as diagnosis planning, working condition selection, judgment method, etc., and belongs to a remote diagnosis method for the typical fault of the DPF.
Specifically, referring to fig. 4, fig. 4 is a schematic structural diagram of a DPF system in the embodiment of the present invention, and as shown in fig. 4, the preset sensor in the embodiment of the present invention includes a temperature sensor, a pressure sensor, and the like, and is connected to a DPF controller and is in communication with a CAN protocol by adding the temperature sensor and the pressure sensor to an inlet and an outlet of the DPF. And the DPF controller is connected with the GPS module to acquire the speed and the geographic position information. Optionally, in the embodiment of the present invention, the data acquisition frequency is 1Hz, and the data format and accuracy are shown in table 1:
Table 1, data acquisition format and precision example table
Optionally, the DPF controller is used for acquiring data only including vehicle speed data, geographical position, temperature and pressure of front and rear ends of the DPF and the like, and uploading the data to the monitoring platform by utilizing the GPRS module for processing and analyzing, so as to establish a typical fault remote diagnosis model algorithm based on speed, temperature and pressure changes.
And step S002, processing and analyzing the original operation condition data to obtain the target operation condition data.
Optionally, the original operating condition data includes feature data corresponding to each sampling point.
Optionally, the step of processing and analyzing the original operating condition data includes:
identifying invalid data, missing data and abnormal data in the characteristic data;
filtering the characteristic data corresponding to each sampling point according to the invalid data, and repairing the invalid data and/or the missing data by a linear interpolation method;
and correcting the abnormal data by a time sequence standard smoothing algorithm.
Optionally, in the embodiment of the invention, the data selection is performed under the working conditions that the vehicle runs at the environment temperature of 2-38 ℃, the altitude is not more than 100km (which is equivalent to the atmospheric pressure of 90 kPa) and the DPF remote transmission data of the vehicle is normal, so that the typical running working conditions are formed. When typical working condition selection is carried out, the influence of unstable performance conditions on fault diagnosis in the initial state of the aftertreatment device is avoided, and DPF (diesel particulate filter) of a vehicle running within two weeks after DPF installation is not diagnosed.
Optionally, the collection of the original operation condition data should be performed within a vehicle operation period of a period of time (for example, 14 days) before the diagnosis point, the operation condition duration may be set according to the actual situation, and includes all continuous data from starting and smooth operation to stopping of the vehicle.
Alternatively, to avoid the impact of unstable performance conditions on fault diagnosis when the DPF device is in an initial state, the original operating condition data may be collected after several (e.g., 5000) pieces of data are run.
Optionally, after the original operation condition data of the DPF is collected, the data can be uploaded to a monitoring platform for processing and analysis, vehicle remote monitoring data meeting diagnosis conditions is selected, data processing is performed according to data processing rules, and target operation condition data is obtained and used for identifying the state of the DPF.
Optionally, the characteristic data includes at least one of position data, vehicle speed data, pressure drop data, temperature data, and exhaust gas flow rate.
Optionally, the invalid data in the feature data includes at least one of:
The speed data with the speed smaller than a first preset speed threshold value when the position data changes or disappears;
vehicle speed data exceeding a second preset vehicle speed threshold;
the vehicle speed data with the difference value between the vehicle speed data and the adjacent data exceeding the preset vehicle speed difference value in the preset acquisition time;
vehicle speed data below a third vehicle speed threshold and having a duration exceeding a preset first duration;
pressure drop data outside of the effective pressure drop threshold range;
continuously exceeding a preset number of sampling points to keep unchanged pressure drop data;
pressure drop data with the difference value between the adjacent data exceeding a preset pressure drop value;
temperature data outside of an effective temperature threshold range;
temperature data of 0 in a vehicle start state;
temperature data maintained at the same value for more than a second preset duration;
temperature data with the difference value exceeding the preset temperature difference value with the adjacent data.
Optionally, in the embodiment of the present invention, the first preset vehicle speed threshold is 2km/h, the second preset vehicle speed threshold is 120km/h, the preset collection time is 1 second, the preset vehicle speed difference is 16km/h, the third vehicle speed threshold is 10km/h, the preset first duration is 5 minutes, the effective pressure drop threshold range and the effective temperature difference threshold range may refer to the above table 1, the preset number of sampling points may be 15 sampling points, the pressure drop data with the adjacent data difference value exceeding the preset pressure drop value may be the pressure drop data with the difference value exceeding the value of 1 sampling point adjacent to the front/rear by more than 50%, the preset second duration is 30 seconds, the temperature data with the adjacent data difference exceeding the preset temperature difference value may be the temperature data with the difference value adjacent to the front/rear by more than 50%, in other embodiments, each value and range may be adjusted according to the actual situation, and the embodiment of the present invention does not form specific limitation to the value and range.
Optionally, the target operation condition data is vehicle remote monitoring data meeting diagnostic conditions, and the judging conditions include: in the statistical period, the data loss rate of three key parameters, such as the vehicle speed, the DPF exhaust temperature and the pressure drop data, is less than 20%, the continuous data loss is not more than 60 seconds, and the specific data processing rules are shown in the table 2:
table 2, data processing rule example table
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Alternatively, when there is a change or disappearance of longitude and latitude (i.e., disappearance of position information), vehicle speed data of which the vehicle speed is less than 2km/h is regarded as invalid data.
Optionally, when the longitude and latitude have the condition of being 0, the corresponding data is directly removed.
Alternatively, the speed under the running condition of long-time traffic jam and intermittent low-speed (the highest speed is less than 10 km/h) is treated as 0.
Alternatively, if the duration exceeds a preset value (e.g., 180 s) at a speed of 0, the vehicle is generally considered to have been stopped at that time, and such data is invalidated and rejected.
Alternatively, data having a duration of low speed (e.g., a maximum vehicle speed of less than 10 km/h) exceeding 5 minutes is treated as invalidation.
Optionally, the step of filtering the feature data corresponding to each sampling point according to the invalid data and repairing the invalid data and/or the missing data by a linear interpolation method includes:
Selecting or determining a sampling point corresponding to the invalid data as an invalid sampling point;
deleting the characteristic data corresponding to the invalid sampling points;
determining adjacent sampling points based on the invalid sampling points and/or sampling points corresponding to the missing data;
and carrying out linear calculation according to the adjacent characteristic data corresponding to the adjacent sampling points to obtain linear difference data for repairing the invalid data and/or the missing data.
Optionally, in the embodiment of the present invention, if invalid data in the position data, the vehicle speed data, the pressure drop data and the temperature data is determined according to the data processing rule, besides directly deleting the determined invalid data, a sampling point corresponding to the invalid data may be determined, all feature data corresponding to the sampling point may be deleted, and then repair processing is performed on the deleted feature data and/or the deleted feature data. For example, when it is identified that the pressure drop data at a certain moment is abnormal, it can be considered that the temperature data at the moment is also abnormal, so that the rest feature data at the sampling point does not need to be judged, and each feature data at the sampling point is directly removed and/or repaired, thereby avoiding waste of time and resources.
Referring to fig. 5, fig. 5 is a schematic diagram of a data processing flow in an embodiment of the present invention, and as shown in fig. 5, performing data restoration in an embodiment of the present invention mainly includes data processing methods such as linear interpolation and smoothing.
Optionally, the missing or invalid data repair is performed by a linear interpolation method, mainly performing linear interpolation on characteristic parameter data points within a preset time range (for example, within a time difference of 2-4 seconds), wherein the characteristic parameters comprise parameters such as running speed, NOx emission and the like. For the characteristic parameter data values with parts obviously exceeding a reasonable range, a front-back average value method is adopted to replace the point.
Optionally, for the data of which the characteristic parameter data is in the effective value range but has partial abnormal points, a time sequence standard smoothing algorithm based on a T4253H filtering method is provided for smoothing, so that the processing effect on nonlinear data is good, and the abnormal points of the data are effectively reduced.
According to the scheme, the original operating condition data of the DPF are collected specifically through a preset sensor; and processing and analyzing the original operation condition data to obtain target operation condition data, wherein invalid data can be deleted by screening and judging the original operation condition data, and the missing data is repaired to obtain the target operation condition data, so that the accuracy and the effectiveness of the target operation condition data can be improved, the target operation condition data obtained by processing and analyzing are used for determining the operation state of the DPF, and the accuracy of the DPF fault diagnosis result can be improved.
Referring to FIG. 6, FIG. 6 is a specific flow chart illustrating the embodiment of FIG. 2 for determining the operating state of a DPF based on target operating condition data of the DPF. The present embodiment is based on the embodiment shown in fig. 2, and in the present embodiment, the step of determining the operation state of the DPF based on the target operation condition data of the DPF includes:
step S101, determining the running state of the DPF according to the pressure drop data after analysis and processing based on a preset matching relation;
optionally, the step of determining the operation state of the DPF according to the pressure drop data after the analysis and processing based on the preset matching relationship further includes:
determining the DPF blocking degree of a first preset step length and each pressure drop value corresponding to the exhaust flow of a second preset step length through preset model software;
and establishing the preset matching relation according to each pressure drop value.
Optionally, the step of determining the operation state of the DPF according to the pressure drop data after the analysis processing based on the preset matching relationship includes:
selecting or determining the exhaust flow corresponding to the pressure drop data after analysis and treatment;
comparing the analyzed pressure drop data and the corresponding exhaust flow with the preset matching relation to determine the blocking degree of the target DPF;
If the target DPF blocking degree is in a first degree interval, judging that the DPF is in a damaged state;
if the target DPF blocking degree is in a second degree interval, judging that the DPF is in a normal state;
and if the target DPF clogging degree is in a third degree interval, judging that the DPF is in a clogging state.
Referring to fig. 7, fig. 7 is a schematic diagram of pressure drop of a DPF in an embodiment of the present invention, as shown in fig. 7, pressure sensors (P1 and P2) are installed at front and rear ends of the DPF, parameters such as data acquisition frequency, data accuracy, response time, etc. are all consistent, and the data acquisition frequency is 1Hz. P1 and P2 are in kpa.
Alternatively, P1 and P2 collected after the engine coolant temperature reached 70℃are valid data.
Optionally, to avoid delays in the P1 and P2 data, the collected P1 and P2 data are first aligned. The alignment method is to determine the effective data length of P1 and P2 by taking the peak value of P1 and P2 as an alignment point when the cooling liquid temperature reaches 70 ℃. Meanwhile, the same temperature processing method is adopted to conduct interpolation processing on abnormal values and missing values in P1 and P2.
Optionally, the fault diagnosis has a certain correlation with the exhaust flow of the engine, and in the embodiment of the invention, a MAP of exhaust flow-fault type-pressure drop performance is established by using a model simulation mode.
Alternatively, the failure type is classified as DPF carrier plugging or DPF carrier breakage.
Referring to fig. 8, fig. 8 is an exemplary MAP diagram provided in an embodiment of the present invention, with a preset model software (e.g., GT-Power), the DPF carrier plugging level D is set to 0%, 5%, 10%. 100% at a first preset step size (step size 5% or specific value increase, n sets of data). In combination with the engine exhaust flow characteristics, the exhaust flow L interval second preset step is set to 50kg/h, 100kg/h, 150kg/h.
When d1=0%, L sets 50 kg/h..400 kg/h, n pressure drop values P-D0 are obtained.
When d2=5%, L sets 50 kg/h..400 kg/h, n pressure drop values P-D1 are obtained.
And the like, obtaining corresponding pressure drop values of n multiplied by m groups, and finally forming a MAP chart of exhaust flow, carrier blockage degree and pressure drop performance through an interpolation method between the data.
Alternatively, in the embodiment of the present invention, the clogging degree of 0% to 10% (i.e., the first degree interval) is regarded as the DPF being in a broken state. The blockage degree of 10-90% (namely, the second degree interval) is regarded as a normal state. The degree of clogging of 90% to 100% (i.e., the third degree interval) is regarded as the DPF being in a clogged state.
Optionally, after embedding MAP (i.e. a preset matching relationship) in the ECU controller of the DPF system, the ECU controller system dynamically feeds back the DPF carrier clogging fault degree by inputting DPF pressure drop change and exhaust flow after data processing to the ECU in real time. When the deviation of the carrier blocking fault degree of the DPF is smaller than 20% or a plurality of step sizes within 60 minutes and the occurrence times are more than 10 times, the occurrence of the carrier blocking of the DPF can be judged, and the carrier blocking degree is prejudged for guiding maintenance.
And step S102, performing linear fitting on the analyzed and processed temperature data to obtain a fitting result, and determining the running state of the DPF according to the fitting result.
Optionally, the step of performing linear fitting on the analyzed temperature data to obtain a fitting result, and determining the running state of the DPF according to the fitting result includes:
calculating a plurality of groups of temperature difference accumulation addition values according to the analyzed and processed temperature data;
and performing linear fitting on the plurality of groups of temperature difference accumulation added values to obtain a fitting result, and determining the running state of the DPF according to the fitting result.
Optionally, the step of linearly fitting the plurality of groups of temperature difference accumulation added values to obtain a fitting result includes:
And performing linear fitting on the plurality of groups of temperature difference accumulation added values according to a least square linear regression method to obtain a fitting result.
Optionally, the step of linearly fitting the plurality of groups of temperature difference accumulation added values according to a least square method linear regression method to obtain the fitting result further includes:
calculating to obtain a first fitting curve slope average value and a first deviation according to fitting results corresponding to a first typical working condition of preset times;
judging whether the first deviation exceeds a preset deviation range or not;
if the first deviation exceeds a preset deviation range, selecting a fitting result corresponding to a second typical working condition of preset times, and calculating to obtain a second fitting curve slope average value and a second deviation;
and if the second deviation exceeds a preset deviation range, judging that the fitting result does not meet a preset judging requirement.
Optionally, a diagnosis is made as to whether the DPF device is removed before a fault determination is made. On the premise that the data of the differential pressure sensor meets the standard quality requirement, if the pressure drop value is suddenly smaller than 0.1kpa and the pressure drop value is unchanged for more than 10 minutes, the DPF can be considered to be dismantled.
Optionally, after confirming the non-dismantling state of the DPF, further judging whether the DPF is blocked or damaged by utilizing the slope change of the straight line fitted by the curve of the accumulated value of the temperature difference before and after the DPF.
Optionally, a DPF temperature difference integrated value Tsum is obtained. Mainly by increasing the DPF inlet temperature (T) with the sampling point sequence (n) in ) And outlet temperature (T) out ) The cumulative sum of the differences (T sum ) The unit is the temperature, and the calculation formula is as follows:
T sum (n)＝T sum (n-1)+[T out (n)-T in (n)] (1)
optionally, linear fitting is carried out on the DPF temperature difference accumulated value according to a least square linear regression method, and the slope k of the obtained fitting curve is obtained. Obtaining n groups of DPF temperature difference accumulated values T by using the formula (1) sum Data.
Optionally, the operating condition includes at least one of a broken condition, a normal condition, and a jammed condition.
Optionally, the step of determining the operation state of the DPF according to the fitting result includes:
selecting or determining the slope of a fitting curve from fitting results meeting the preset judging requirements;
comparing the slope of the fitted curve with a preset threshold, wherein the preset threshold comprises a first slope threshold and a second slope threshold;
if the slope of the fitted curve is larger than the first slope threshold, judging that the DPF is in a damaged state;
If the slope of the fitted curve is between the first slope threshold and the second slope threshold, judging that the DPF is in a normal state;
and if the slope of the fitted curve is smaller than the second slope threshold value, judging that the DPF is in a blocking state.
Alternatively, for a given set of data (xi, yi), it is assumed that it satisfies the polynomial of degree n:
to find the optimal solution for each order parameter, the sum of squares of the difference between the values calculated by the polynomial of degree n and yi should be minimal for each xi, i.e.:
by adopting the method and the actual test, the recommended limit value for judging the DPF fault blocking or breakage fault, namely a first slope threshold value and a second slope threshold value, can be determined, wherein the first slope threshold value recommended in the embodiment of the invention is-3, the second slope threshold value is-20, and when the slope of the accumulated temperature difference curve before and after the DPF is greater than-3, the DPF is in a breakage state; when the slope is between-3 and-20, the DPF is in a normal running state; when the slope is less than-20, the DPF is in a blocking state.
Referring to fig. 9, fig. 9 is a graph illustrating the slope of a fitted curve in the embodiment of the present invention, as shown in fig. 9, after invalid data rejection processing is performed on data in typical diagnostic conditions according to the requirements in the foregoing embodiment, 3 typical conditions are formed, and DPF pressure drop data, DPF outlet and inlet temperature data are read and analyzed according to the conditions, so as to calculate a slope k of a fitted curve of a DPF temperature difference integrated value under each condition.
Optionally, in the failure diagnosis process, a diagnosis is first made as to whether the DPF device is removed. If the DPF pressure drop data is maintained at 0.1kPa or less for 10 minutes or more continuously on the premise that the differential pressure sensor data satisfies the quality requirements in the foregoing embodiments, it can be judged that the DPF is removed and alarm information is generated.
Optionally, after the non-dismantling state of the DPF is confirmed, whether the DPF is blocked or damaged is further judged by utilizing the change condition of the slope k of the accumulated value curve of the temperature difference before and after the DPF.
Alternatively, the slope k average and deviation of the DPF temperature difference cumulative value fitting curve under 3 typical working conditions are calculated. If the deviation is greater than +/-15%, 1 typical working condition is selected after the existing 3 typical working condition time sequences, and the average value and the deviation of the slope k of the DPF temperature difference cumulative value fitting curve under the 3 typical working conditions are calculated. And if the deviation of the k value calculated for the second time is still more than +/-15%, judging that the k value does not meet the judgment requirement and judging.
Referring to fig. 10, fig. 10 is a schematic diagram of actual measurement results in the embodiment of the present invention, and as shown in fig. 10, taking an actual measurement analysis result of 100 vehicles as an example, except that the slope of a vehicle temperature difference accumulation curve in the state of DPF breakage and blockage is [ -20, -3], the data points of the normal vehicles of the remaining DPF can fall within [ -20, -3], which verifies that the DPF fault diagnosis method provided in the embodiment of the present invention has sufficient accuracy and effectiveness.
According to the embodiment, through the scheme, particularly, the temperature data after analysis and treatment are subjected to linear fitting, so that a fitting result is obtained; and determining the running state of the DPF according to the fitting result, and realizing real-time monitoring of the running state of the DPF so as to accurately diagnose the fault of the DPF in time and further take corresponding treatment measures, thereby ensuring the stable running of the DPF.
In addition, an embodiment of the present invention further provides a DPF failure diagnosis apparatus, including:
and the identification module is used for determining the running state of the DPF based on the target running condition data of the DPF when the DPF of the diesel vehicle particulate filter is in a non-dismantling state.
The principle and implementation process of the DPF fault diagnosis are implemented in this embodiment, please refer to the above embodiments, and are not described herein.
In addition, the embodiment of the invention also provides a terminal device, which comprises a memory, a processor and a DPF fault diagnosis program stored on the memory and capable of running on the processor, wherein the DPF fault diagnosis program realizes the steps of the DPF fault diagnosis method when being executed by the processor.
Because the DPF fault diagnosis program is executed by the processor, all the technical solutions of all the embodiments are adopted, and therefore, at least all the beneficial effects brought by all the technical solutions of all the embodiments are provided, and are not described in detail herein.
In addition, the embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a DPF fault diagnosis program, and the DPF fault diagnosis program realizes the steps of the DPF fault diagnosis method when being executed by a processor.
Because the DPF fault diagnosis program is executed by the processor, all the technical solutions of all the embodiments are adopted, and therefore, at least all the beneficial effects brought by all the technical solutions of all the embodiments are provided, and are not described in detail herein.
Compared with the prior art, the DPF fault diagnosis method, the device, the terminal equipment and the storage medium provided by the embodiment of the invention have the advantages that the running state of the DPF is determined based on the target running condition data of the DPF when the DPF of the diesel vehicle particulate matter catcher is in a non-dismantling state, so that the running state of the DPF is monitored in real time, the DPF fault can be diagnosed accurately in time, and corresponding treatment measures are taken, so that the stable running of the DPF can be ensured.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as above, including several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, a controlled terminal, or a network device, etc.) to perform the method of each embodiment of the present application.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (16)
1. A DPF failure diagnosis method, characterized by comprising the steps of:
and determining the running state of the DPF based on the target running condition data of the DPF under the condition that the DPF is in a non-dismantling state.
2. The DPF fault diagnosis method according to claim 1, wherein the step of determining the operating state of the DPF based on the target operating condition data of the DPF while the DPF is in a non-removed state further comprises:
acquiring target operation condition data of the DPF; and/or the number of the groups of groups,
determining whether the DPF is in a non-removed state based on the target operating condition data.
3. The DPF fault diagnosis method according to claim 2, wherein the step of acquiring target operating condition data of the DPF includes:
acquiring original operation condition data of the DPF through a preset sensor;
and processing and analyzing the original operation condition data to obtain the target operation condition data.
4. The DPF fault diagnosis method according to claim 3, wherein the original operating condition data includes characteristic data corresponding to each sampling point, and the step of performing processing analysis on the original operating condition data includes:
Identifying invalid data, missing data and abnormal data in the characteristic data;
filtering the characteristic data corresponding to each sampling point according to the invalid data, and repairing the invalid data and/or the missing data by a linear interpolation method;
and correcting the abnormal data by a time sequence standard smoothing algorithm.
5. The DPF failure diagnosis method according to claim 4, wherein the characteristic data includes at least one of position data, vehicle speed data, pressure drop data, temperature data, and exhaust gas flow rate, and the invalid data in the characteristic data includes at least one of:
the speed data with the speed smaller than a first preset speed threshold value when the position data changes or disappears;
vehicle speed data exceeding a second preset vehicle speed threshold;
the vehicle speed data with the difference value between the vehicle speed data and the adjacent data exceeding the preset vehicle speed difference value in the preset acquisition time;
vehicle speed data below a third vehicle speed threshold and having a duration exceeding a preset first duration;
pressure drop data outside of the effective pressure drop threshold range;
continuously exceeding a preset number of sampling points to keep unchanged pressure drop data;
pressure drop data with the difference value between the adjacent data exceeding a preset pressure drop value;
Temperature data outside of an effective temperature threshold range;
temperature data of 0 in a vehicle start state;
temperature data maintained at the same value for more than a second preset duration;
temperature data with the difference value exceeding the preset temperature difference value with the adjacent data.
6. The DPF failure diagnosis method according to claim 4, wherein the step of filtering the feature data corresponding to each sampling point according to the invalid data and performing data restoration of the invalid data and/or missing data by a linear interpolation method includes:
selecting or determining a sampling point corresponding to the invalid data as an invalid sampling point;
deleting the characteristic data corresponding to the invalid sampling points;
determining adjacent sampling points based on the invalid sampling points and/or sampling points corresponding to the missing data;
and carrying out linear calculation according to the adjacent characteristic data corresponding to the adjacent sampling points to obtain linear difference data for repairing the invalid data and/or the missing data.
7. The DPF failure diagnosis method according to claim 2, characterized in that the step of determining whether the DPF is in a non-removed state based on the target operating condition data includes:
Judging whether pressure drop data which is smaller than a preset pressure drop threshold value and the duration exceeds a preset time threshold value exists in the target operation condition data;
if the pressure drop data which is smaller than a preset pressure drop threshold value and the duration exceeds a preset time threshold value exists in the target operation condition data, judging that the DPF is in a dismantling state;
and if no pressure drop data which is smaller than a preset pressure drop threshold value and the duration exceeds a preset time threshold value exists in the target operation condition data, judging that the DPF is in a non-dismantling state.
8. The DPF failure diagnosis method according to any one of claims 1 to 7, characterized in that the target operating condition data includes pressure drop data and/or temperature data after analysis processing, and the step of determining the operating state of the DPF based on the target operating condition data of the DPF includes:
determining the running state of the DPF according to the pressure drop data after analysis and processing based on a preset matching relation; and/or the number of the groups of groups,
calculating a plurality of groups of temperature difference accumulation addition values according to the analyzed and processed temperature data;
and performing linear fitting on the plurality of groups of temperature difference accumulation added values to obtain a fitting result, and determining the running state of the DPF according to the fitting result.
9. The DPF fault diagnosis method according to claim 8, wherein the step of determining the operating state of the DPF from the analyzed and processed pressure drop data based on a preset matching relationship further includes:
determining the DPF blocking degree of a first preset step length and each pressure drop value corresponding to the exhaust flow of a second preset step length through preset model software;
and establishing the preset matching relation according to each pressure drop value.
10. The DPF fault diagnosis method according to claim 8, wherein the step of determining the operating state of the DPF from the analyzed and processed pressure drop data based on a preset matching relationship includes:
selecting or determining the exhaust flow corresponding to the pressure drop data after analysis and treatment;
comparing the analyzed pressure drop data and the corresponding exhaust flow with the preset matching relation to determine the blocking degree of the target DPF;
if the target DPF blocking degree is in a first degree interval, judging that the DPF is in a damaged state;
if the target DPF blocking degree is in a second degree interval, judging that the DPF is in a normal state;
and if the target DPF clogging degree is in a third degree interval, judging that the DPF is in a clogging state.
11. The DPF fault diagnosis method of claim 8, wherein the step of linearly fitting the plurality of sets of temperature difference accumulation addition values to obtain fitting results includes:
and performing linear fitting on the plurality of groups of temperature difference accumulation added values according to a least square linear regression method to obtain a fitting result.
12. The DPF fault diagnosis method according to claim 11, wherein the step of linearly fitting the plurality of sets of temperature difference accumulation addition values according to a least square method further includes, after the step of obtaining the fitting result:
calculating to obtain a first fitting curve slope average value and a first deviation according to fitting results corresponding to a first typical working condition of preset times;
judging whether the first deviation exceeds a preset deviation range or not;
if the first deviation exceeds a preset deviation range, selecting a fitting result corresponding to a second typical working condition of preset times, and calculating to obtain a second fitting curve slope average value and a second deviation;
and if the second deviation exceeds a preset deviation range, judging that the fitting result does not meet a preset judging requirement.
13. The DPF fault diagnosis method of claim 12, wherein the step of determining the operating state of the DPF based on the fitting result includes:
Selecting or determining the slope of a fitting curve from fitting results meeting the preset judging requirements;
comparing the slope of the fitted curve with a preset threshold, wherein the preset threshold comprises a first slope threshold and a second slope threshold;
if the slope of the fitted curve is larger than the first slope threshold, judging that the DPF is in a damaged state;
if the slope of the fitted curve is between the first slope threshold and the second slope threshold, judging that the DPF is in a normal state;
and if the slope of the fitted curve is smaller than the second slope threshold value, judging that the DPF is in a blocking state.
14. A DPF failure diagnosis apparatus, characterized by comprising:
and the identification module is used for determining the running state of the DPF based on the target running condition data of the DPF when the DPF of the diesel vehicle particulate filter is in a non-dismantling state.
15. A terminal device, characterized in that it comprises a memory, a processor and a DPF fault diagnosis program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the DPF fault diagnosis method according to any one of claims 1-13.
16. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a DPF failure diagnosis program which, when executed by a processor, implements the steps of the DPF failure diagnosis method according to any one of claims 1 to 13.
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