CN113447290A - Engineering machinery fault early warning method and device and engineering machinery - Google Patents
Engineering machinery fault early warning method and device and engineering machinery Download PDFInfo
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Abstract
The invention provides a fault early warning method and device for engineering machinery and the engineering machinery, wherein the method comprises the following steps: acquiring historical use data and historical operation data of the target engineering machinery; counting the occurrence frequency and the occurrence time of preset fault keywords based on historical use data; performing collaborative filtering on historical operating data based on the occurrence time of a preset fault keyword in the historical using data to determine abnormal operating parameters; predicting fault prediction data of the target engineering machinery in the next operation period based on the occurrence frequency, the occurrence time and the abnormal operation parameters of preset fault keywords; and carrying out fault early warning based on the fault prediction data. Through the use condition and the subjective evaluation of the engineering machinery, which are fed back by a user of the engineering machinery, and the corresponding operation data of the engineering machinery, the abnormity or the fault with the fault risk is accurately positioned, so that the fault early warning is timely carried out, the accuracy of the fault early warning is improved, the timely maintenance of the engineering machinery is facilitated, and the service life is prolonged.
Description
Technical Field
The invention relates to the technical field of engineering machinery, in particular to a fault early warning method and device for engineering machinery and the engineering machinery.
Background
Construction machines such as excavators play a very important role in construction. The fault detection and the fault early warning of the engineering machinery not only influence the service life of the engineering machinery, but also have important influence on the completion progress of the engineering construction project.
In order to solve the problem of fault early warning of the engineering machinery, in the prior art, operation log data of the engineering machinery is uploaded to a cloud platform, and whether the engineering machinery breaks down or not is determined by utilizing big data analysis.
Disclosure of Invention
In view of this, the embodiment of the invention provides an engineering machine fault early warning method and device and an engineering machine to overcome the problem of low engineering machine fault early warning accuracy in the prior art.
According to a first aspect, an embodiment of the present invention provides an engineering machine fault early warning method, including:
acquiring historical use data and historical operation data of a target engineering machine, wherein the historical use data is subjective evaluation data used for representing the use condition of the target engineering machine, and the historical operation data is parameter data used for representing the operation state of the target engineering machine;
counting the occurrence frequency and the occurrence time of a preset fault keyword based on the historical use data, wherein the preset fault keyword is used for representing the abnormal state of the target engineering machinery;
performing collaborative filtering on the historical operating data based on the occurrence time of a preset fault keyword in the historical using data to determine abnormal operating parameters;
predicting fault prediction data of the target engineering machinery in the next operation period based on the occurrence frequency, the occurrence time and the abnormal operation parameters of preset fault keywords;
and carrying out fault early warning based on the fault prediction data.
Optionally, the predicting fault prediction data of the target engineering machine in the next operation cycle based on the occurrence frequency and the occurrence time of the preset fault keyword and the corresponding abnormal operation parameter includes:
predicting the occurrence frequency of a preset fault keyword in the next operation period based on the occurrence frequency and the occurrence time of the preset fault keyword;
and predicting the parameter value of the abnormal operation parameter in the next operation period based on the abnormal operation parameter.
Optionally, a filtering smoothing algorithm and a time series model are adopted to predict the occurrence frequency of the preset fault key words in the next operation period and the parameter value of the abnormal operation parameter in the next operation period.
Optionally, the performing fault early warning based on the fault prediction data includes:
determining the current fault grade of the target engineering machinery based on the relation between the predicted parameter value of the abnormal operation parameter and a preset parameter threshold value and the relation between the predicted occurrence frequency of the fault keyword and a preset frequency threshold value;
and carrying out fault early warning based on the current fault grade.
Optionally, the method further comprises:
and generating a maintenance scheme based on the fault prediction data.
Optionally, the method further comprises:
acquiring a first position of the target engineering machinery and a second position of a maintenance terminal;
generating a maintenance route based on the first location and the second location;
and sending the maintenance route and the maintenance scheme to the maintenance terminal.
According to a second aspect, an embodiment of the present invention provides an engineering machine fault early warning apparatus, including:
the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring historical use data and historical operation data of the target engineering machinery, the historical use data is subjective evaluation data used for representing the use condition of the target engineering machinery, and the historical operation data is parameter data used for representing the operation state of the target engineering machinery;
the first processing module is used for counting the occurrence frequency and the occurrence time of a preset fault keyword based on the historical use data, wherein the preset fault keyword is used for representing the abnormal state of the target engineering machinery;
the second processing module is used for performing collaborative filtering on the historical operating data based on the occurrence time of a preset fault keyword in the historical using data to determine an abnormal operating parameter;
the third processing module is used for predicting fault prediction data of the target engineering machinery in the next operation period based on the occurrence frequency, the occurrence time and the abnormal operation parameters of preset fault keywords;
and the fourth processing module is used for carrying out fault early warning based on the fault prediction data.
According to a third aspect, an embodiment of the present invention provides a construction machine, including: a memory and a processor, the memory and the processor being communicatively coupled to each other, the memory having stored therein computer instructions, and the processor performing the method of the first aspect, or any one of the optional embodiments of the first aspect, by executing the computer instructions.
Optionally, the work machine is an excavator.
According to a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the method of the first aspect, or any one of the optional implementation manners of the first aspect.
The technical scheme of the invention has the following advantages:
according to the engineering machinery fault early warning method, the engineering machinery fault early warning device and the engineering machinery provided by the embodiment of the invention, historical use data and historical operation data of target engineering machinery are obtained; counting the occurrence frequency and the occurrence time of preset fault keywords based on historical use data; performing collaborative filtering on historical operating data based on the occurrence time of a preset fault keyword in the historical using data to determine abnormal operating parameters; predicting fault prediction data of the target engineering machinery in the next operation period based on the occurrence frequency, the occurrence time and the abnormal operation parameters of preset fault keywords; and carrying out fault early warning based on the fault prediction data. Therefore, the state of the engineering machinery which cannot be monitored is analyzed through sparse data such as the service condition, subjective evaluation and the like of the engineering machinery fed back by a user of the engineering machinery, meanwhile, high-frequency data such as corresponding operation data of the engineering machinery are combined, and through fusion of the low-frequency data and the high-frequency data, abnormity or faults with fault risks can be accurately positioned, so that fault early warning is timely carried out, the accuracy of the fault early warning is improved, and the engineering machinery is favorably maintained in time to prolong the service life.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flowchart of a fault warning method for an engineering machine according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a working process of engineering machinery fault early warning according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an engineering machine fault early warning device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a construction machine according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The technical features mentioned in the different embodiments of the invention described below can be combined with each other as long as they do not conflict with each other.
Construction machines such as excavators play a very important role in construction. The fault detection and the fault early warning of the engineering machinery not only influence the service life of the engineering machinery, but also have important influence on the completion progress of the engineering construction project.
In order to solve the problem of fault early warning of the engineering machinery, in the prior art, operation log data of the engineering machinery is uploaded to a cloud platform, and whether the engineering machinery breaks down or not is determined by utilizing big data analysis.
Based on the above problem, an embodiment of the present invention provides an engineering machine fault early warning method, as shown in fig. 1, the engineering machine fault early warning method provided by the embodiment of the present invention specifically includes the following steps:
step S101: and acquiring historical use data and historical operation data of the target engineering machinery.
The historical use data is subjective evaluation data used for representing the use condition of the target engineering machine, and the historical operation data is parameter data used for representing the operation state of the target engineering machine. In the embodiment of the present invention, the target construction machine is described as an excavator, but in practical applications, the target construction machine may be another construction machine such as a concrete transport vehicle, a crane, a forklift, and the present invention is not limited thereto.
Illustratively, the historical use data is a use log fed back by a user of the target construction machine, and the use log comprises the use condition of the excavator fed back by the user at regular time, the reporting condition of the fault at irregular time, the subjective evaluation of an operator on the excavator and the like. The data can be used for analyzing the running state of the excavator, which cannot be directly sensed by monitoring equipment such as a sensor arranged on the excavator, and reflecting the running state of the excavator, possible fault hidden dangers and the like from the visual angle of use of a user; the historical operation data is operation data collected by operation monitoring equipment arranged on the excavator, such as: engine speed, oil consumption, cooling water temperature and other parameters.
Step S102: and counting the occurrence frequency and the occurrence time of the preset fault keywords based on the historical use data.
The preset fault keywords are used for representing the abnormal state of the target engineering machinery. Specifically, the preset fault keyword may be one or multiple, the occurrence frequency of the preset fault keyword may be counted separately, or the trip frequencies of all the preset fault keywords may be counted comprehensively, it should be noted that, in the embodiment of the present invention, the description is given by taking the separate statistics of different preset fault keywords as an example, and the present invention is not limited thereto.
For example, for an excavator, the preset fault keyword includes but is not limited to: the engine shakes, emits black smoke, does not have any force, falls off arms and the like, and particularly, the incidence relation between words appearing in historical use data and engineering machinery faults can be determined through big data analysis to flexibly set preset fault keywords, and the invention is not limited by the method.
Step S103: and performing collaborative filtering on the historical operating data based on the occurrence time of the preset fault keyword in the historical using data to determine abnormal operating parameters.
Wherein, the preset fault keywords appear in the historical use data as follows: when the 'black smoke' is emitted, the fact that the excavator is possibly abnormal at the moment is indicated, and in order to further confirm the working state of the excavator, the 'black smoke' and some abnormal operation parameters of the excavator can be analyzed from historical operation data by utilizing a collaborative filtering algorithm according to the time when the 'black smoke' is emitted. Illustratively, the abnormal operation parameters include: for example, the engine speed is too high, the cooling water temperature is too high, and the like, and the present invention is not limited thereto.
Step S104: and predicting fault prediction data of the target engineering machinery in the next operation period based on the occurrence frequency, the occurrence time and the abnormal operation parameters of the preset fault keywords.
The fault prediction data is used for representing the prediction condition of the target engineering machine which is possible to have a fault condition in the next operation period. Illustratively, the fault prediction data includes: the occurrence frequency of fault keywords, specific parameter values of abnormal operation parameters and the like are preset in the next operation period.
Step S105: and carrying out fault early warning based on the fault prediction data.
By executing the steps, the engineering machine fault early warning method provided by the embodiment of the invention analyzes the state of the engineering machine which cannot be monitored by using sparse data such as the use condition and subjective evaluation of the engineering machine fed back by a user of the engineering machine, and can accurately position the abnormity or fault with fault risk by combining high-frequency data such as corresponding operation data of the engineering machine and fusing the low-frequency data and the high-frequency data, so that the fault early warning is timely performed, the accuracy of the fault early warning is improved, and the engineering machine can be timely maintained to prolong the service life.
Specifically, in an embodiment, the step S104 specifically includes the following steps:
step S201: and predicting the occurrence frequency of the preset fault keyword in the next operation period based on the occurrence frequency and the occurrence time of the preset fault keyword.
Specifically, the operation period can be flexibly set according to the actual maintenance requirement and the requirement of the early warning response speed of the excavator, the operation period is 1 month in the embodiment of the present invention, and the operation period can be three months, 10 days, half a month, and the like in practical application, which is not limited in the present invention. Specifically, based on the occurrence frequency and the occurrence time of the preset fault keyword, the occurrence frequency of the preset fault keyword in each historical operation period can be determined, so that the regularity trend of the occurrence of the preset keyword when the excavator continues to operate according to the current state can be obtained according to the historical operation period, and the occurrence frequency of the next operation period can be estimated.
Step S202: and predicting the parameter value of the abnormal operation parameter in the next operation period based on the abnormal operation parameter.
Specifically, the abnormal operation parameter is a condition that the normal operation of the excavator is not met, for example, the cooling water temperature required by the normal operation of the excavator is not higher than 25 ℃, and if the current cooling water temperature is 30 ℃, the cooling water temperature is indicated as the abnormal operation parameter.
For example, a filter smoothing algorithm and a time series model may be used to predict the occurrence frequency of the preset fault keyword in the next operating period and the parameter value of the abnormal operating parameter in the next operating period. The filtering smoothing algorithm can eliminate a small amount of data with extremely violent change, weaken the irregular fluctuation of unstable and continuously-changed time sequence data, and predict the change trend of the key parameter by using the time sequence, thereby more accurately estimating the next change direction of the key parameter. The specific working principle and the execution process of the related algorithm can refer to the related description in the prior art, and are not described herein again.
Specifically, in an embodiment, the step S105 specifically includes the following steps:
step S301: and determining the current fault grade of the target engineering machinery based on the relation between the predicted parameter value of the abnormal operation parameter and the preset parameter threshold value and the relation between the predicted occurrence frequency of the fault keyword and the preset frequency threshold value.
The preset parameter threshold is a parameter peak value when the abnormal operation parameter is in normal operation, and similarly, the preset frequency threshold is a frequency peak value of a preset fault keyword when the excavator is in normal operation. The more the parameter value exceeds the preset parameter threshold value, the more serious the abnormality of the excavator is, the higher the probability of possible fault is, and similarly, the more the occurrence frequency of the preset fault keyword is higher than the frequency peak value, the more serious the abnormality of the excavator is also, the higher the probability of possible fault is, and the current fault level of the excavator can be determined by combining the abnormal conditions reflected by the two.
Step S301: and carrying out fault early warning based on the current fault grade. In the embodiment of the invention, three levels are taken as an example, the first level indicates that the excavator has no fault, the second level indicates that the excavator has a slight fault, the third level indicates that the excavator has a serious fault and the like, and the early warning is carried out when the current fault level is greater than the first level so as to remind maintainers to overhaul in time, avoid fault expansion and prolong the service life of the excavator. In addition, in practical application, the fault level may be further divided into more levels, and the fault pre-warning is performed after the fault reaches a certain fault level, for example, the pre-warning is performed only when the fault reaches the highest level, and the invention is not limited thereto.
Specifically, in practical application, a computer program for implementing the engineering machinery fault early warning method can be packaged and compiled into a model file to generate a corresponding cloud model, the cloud model can be downloaded and refreshed in one key through a mobile phone two-dimensional code scanning login system, and the model file can also be filled into an excavator by using a mobile phone. The model file can be automatically updated at certain time intervals, such as one month or three months, and the latest historical data is included in the model file. Therefore, automatic early warning of the engineering machinery can be realized on the engineering machinery side under the condition that an external cloud server is not required. The problem that the excavator model is difficult to update under special working conditions such as tunnels and mines is solved.
Specifically, in an embodiment, the method for early warning of a fault of an engineering machine specifically includes the following steps:
step S106: and generating a maintenance scheme based on the fault prediction data.
Specifically, if the engine is supposed to be out of order according to the abnormal operation parameters included in the failure prediction data, such as the over-high rotating speed of the engine, a maintenance scheme for overhauling the engine is correspondingly generated, maintenance personnel can conveniently and pertinently maintain the excavator, and the maintenance efficiency is improved.
Step S107: and acquiring a first position of the target engineering machine and a second position of the maintenance terminal.
The maintenance terminal is a user terminal carried by a maintenance worker, and can be a terminal device such as a mobile phone, the current position of the maintenance worker can be obtained by utilizing the positioning function of the maintenance terminal, and similarly, the current position of the excavator is obtained by arranging a positioning device on the excavator.
Step S108: a maintenance route is generated based on the first location and the second location.
Specifically, the current path planning algorithm can be adopted to combine with a map of a construction site to obtain the nearest maintenance route for maintenance personnel to move to the target excavator, so that the time consumption is shortened, and the maintenance efficiency of the excavator is further improved.
Step S109: and sending the maintenance route and the maintenance scheme to a maintenance terminal.
Specifically, the maintenance route and the maintenance scheme are sent to the maintenance terminal corresponding to the maintenance personnel, so that the maintenance personnel can maintain the excavator in time, and in an actual working condition, when the maintenance personnel face a plurality of to-be-maintained engineering machines, the optimal maintenance scheme can be arranged according to the current fault severity, the time consumption of the maintenance route and the like, so that the maintenance efficiency of the engineering machines is further improved.
The engineering machinery fault early warning method provided by the embodiment of the invention will be described in detail below with reference to specific application examples.
The working process of the engineering machinery fault early warning is shown in fig. 2, a user side problem rapid reporting means and a generalized fault recording template are established on an excavator, so that an operator can record use data in the process of daily using the excavator, and a sparse data matrix is formed; and collecting the parameter values of each key parameter in the running process of the excavator in real time to form a high-frequency data matrix. And then, after data fusion processing of the sparse data matrix and the high-frequency data, a decision suggestion of a maintenance plan is obtained, and a fault early warning function of the engineering machinery is realized.
The working principle is as follows: the method comprises the steps that the user feeds back the using condition of the excavator and reports faults irregularly at regular time, subjective evaluation of an operator on the working condition of the excavator is obtained, and the state which cannot be directly sensed by a sensor can be analyzed; monitoring core parameters of a power system and a hydraulic system of the excavator by using high-frequency data, diagnosing common obvious faults, and sensing states which cannot be monitored by a sensor by using low-frequency data, such as shaking of an engine, black smoke emission, weak action, arm falling and the like; counting the word frequency of the keywords in the log uploaded by an operator, rejecting a small amount of data with extremely violent change based on a filtering smoothing algorithm and a time sequence, weakening the irregular fluctuation of unstable and continuously-changed time sequence data, and predicting the change trend of key parameters by using the time sequence; meanwhile, processing operation data in the same time range based on a collaborative filtering algorithm, and performing collaborative filtering with collected high-frequency data representing the working condition of the excavator by using user feedback in the period of time, such as weakness of the excavator, so as to analyze the most risky parameter abnormality or fault state, such as engine stall, low output power of a hydraulic pump and the like, and estimate the next change direction of the key parameter; according to the statistical analysis result, calculating the frequency of abnormal reporting and the deviation of an abnormal value from a normal threshold, judging the severity of the abnormality to obtain a maintenance scheme, then according to the time-space distribution of an operator and the current position of a maintenance engineer, counting all feasible routes and the time consumed by the maintenance engineer to reach all excavator sites, and adding the time required by maintenance estimated according to the past experience of the engineer, thereby calculating the maintenance route with the shortest total time consumption, and automatically recommending a maintenance plan for the maintenance engineer.
According to the scheme, the subjective logs fed back by an operator are based on high-frequency historical operating data, so that the flexibility is high, the complete working condition of the excavator can be covered, the deviation of functional parameters can be accurately found, and the state which cannot be directly monitored by a sensor can be excavated; by utilizing the fusion analysis of high-frequency and low-frequency data, the relation between the fault and the log of the operator is excavated; establishing a smooth filtering and collaborative filtering algorithm based on the word frequency of the keywords in the operation log to find out the most risky abnormity or fault; the mobile phone login control model is used for updating, and the mobile phone can also be used for filling the model for the excavator, so that the problem that the excavator model is difficult to update under special working conditions such as tunnels and mines is solved. And taking the abnormal severity and the time-space distribution of the excavator as decision bases, calculating a most time-saving maintenance route, and automatically pushing an optimal maintenance suggestion for a maintenance engineer.
By executing the steps, the engineering machine fault early warning method provided by the embodiment of the invention analyzes the state of the engineering machine which cannot be monitored by using sparse data such as the use condition and subjective evaluation of the engineering machine fed back by a user of the engineering machine, and can accurately position the abnormity or fault with fault risk by combining high-frequency data such as corresponding operation data of the engineering machine and fusing the low-frequency data and the high-frequency data, so that the fault early warning is timely performed, the accuracy of the fault early warning is improved, and the engineering machine can be timely maintained to prolong the service life. And taking the abnormal severity and the space-time distribution of the engineering machinery as a decision basis, calculating a most time-saving maintenance route and a maintenance scheme, automatically pushing an optimal maintenance suggestion for a maintenance engineer, and improving the maintenance efficiency.
An embodiment of the present invention further provides an engineering machine fault early warning device, as shown in fig. 3, the engineering machine fault early warning device specifically includes:
the obtaining module 101 is configured to obtain historical use data and historical operation data of the target engineering machine, where the historical use data is subjective evaluation data used for representing a use condition of the target engineering machine, and the historical operation data is parameter data used for representing an operation state of the target engineering machine. For details, refer to the related description of step S101 in the above method embodiment, and no further description is provided here.
The first processing module 102 is configured to count occurrence frequency and occurrence time of a preset fault keyword based on historical usage data, where the preset fault keyword is used to represent an abnormal state of the target engineering machine. For details, refer to the related description of step S102 in the above method embodiment, and no further description is provided here.
And the second processing module 103 is configured to perform collaborative filtering on the historical operating data based on the occurrence time of the preset fault keyword in the historical usage data, and determine an abnormal operating parameter. For details, refer to the related description of step S103 in the above method embodiment, and no further description is provided here.
And the third processing module 104 is configured to predict fault prediction data of the target engineering machine in the next operation cycle based on the occurrence frequency, the occurrence time, and the abnormal operation parameter of the preset fault keyword. For details, refer to the related description of step S104 in the above method embodiment, and no further description is provided here.
And the fourth processing module 105 is configured to perform fault early warning based on the fault prediction data. For details, refer to the related description of step S105 in the above method embodiment, and no further description is provided here.
The engineering machine fault early warning device provided by the embodiment of the invention is used for executing the engineering machine fault early warning method provided by the embodiment, the implementation mode and the principle are the same, and the detailed content refers to the related description of the method embodiment and is not repeated.
Through the cooperative cooperation of the components, the engineering machine fault early warning device provided by the embodiment of the invention analyzes the state of the engineering machine which cannot be monitored by using sparse data such as the use condition and subjective evaluation of the engineering machine fed back by a user of the engineering machine, and simultaneously combines high-frequency data such as corresponding operating data of the engineering machine, and can accurately position the abnormity or fault with fault risk by fusing the low-frequency data and the high-frequency data, so as to perform fault early warning in time, thereby improving the accuracy of the fault early warning, and being beneficial to performing the timely maintenance on the engineering machine to prolong the service life.
An embodiment of the present invention further provides an engineering machine, as shown in fig. 4, the engineering machine includes: a processor 901 and a memory 902, wherein the processor 901 and the memory 902 may be connected by a bus or by other means, and fig. 4 illustrates an example of a connection by a bus.
The memory 902, which is a non-transitory computer-readable storage medium, may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the methods in the above-described method embodiments. The processor 901 executes various functional applications and data processing of the processor by executing non-transitory software programs, instructions and modules stored in the memory 902, that is, implements the methods in the above-described method embodiments.
The memory 902 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 901, and the like. Further, the memory 902 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 902 may optionally include memory located remotely from the processor 901, which may be connected to the processor 901 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
One or more modules are stored in the memory 902, which when executed by the processor 901 performs the methods in the above-described method embodiments.
The specific details of the controller may be understood by referring to the corresponding related descriptions and effects in the above method embodiments, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, and the implemented program can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (10)
1. A fault early warning method for engineering machinery is characterized by comprising the following steps:
acquiring historical use data and historical operation data of a target engineering machine, wherein the historical use data is subjective evaluation data used for representing the use condition of the target engineering machine, and the historical operation data is parameter data used for representing the operation state of the target engineering machine;
counting the occurrence frequency and the occurrence time of a preset fault keyword based on the historical use data, wherein the preset fault keyword is used for representing the abnormal state of the target engineering machinery;
performing collaborative filtering on the historical operating data based on the occurrence time of a preset fault keyword in the historical using data to determine abnormal operating parameters;
predicting fault prediction data of the target engineering machinery in the next operation period based on the occurrence frequency, the occurrence time and the abnormal operation parameters of preset fault keywords;
and carrying out fault early warning based on the fault prediction data.
2. The method according to claim 1, wherein predicting the fault prediction data of the target engineering machine in the next operation cycle based on the occurrence frequency and the occurrence time of the preset fault keyword and the corresponding abnormal operation parameter comprises:
predicting the occurrence frequency of a preset fault keyword in the next operation period based on the occurrence frequency and the occurrence time of the preset fault keyword;
and predicting the parameter value of the abnormal operation parameter in the next operation period based on the abnormal operation parameter.
3. The method of claim 2, wherein the fault pre-warning based on the fault prediction data comprises:
determining the current fault grade of the target engineering machinery based on the relation between the predicted parameter value of the abnormal operation parameter and a preset parameter threshold value and the relation between the predicted occurrence frequency of the fault keyword and a preset frequency threshold value;
and carrying out fault early warning based on the current fault grade.
4. The method of claim 1, further comprising:
and generating a maintenance scheme based on the fault prediction data.
5. The method of claim 4, further comprising:
acquiring a first position of the target engineering machinery and a second position of a maintenance terminal;
generating a maintenance route based on the first location and the second location;
and sending the maintenance route and the maintenance scheme to the maintenance terminal.
6. The method of claim 2,
and predicting the occurrence frequency of the preset fault key words in the next operation period and the parameter values of the abnormal operation parameters in the next operation period by adopting a filtering smoothing algorithm and a time sequence model.
7. The utility model provides an engineering machine tool trouble early warning device which characterized in that includes:
the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring historical use data and historical operation data of the target engineering machinery, the historical use data is subjective evaluation data used for representing the use condition of the target engineering machinery, and the historical operation data is parameter data used for representing the operation state of the target engineering machinery;
the first processing module is used for counting the occurrence frequency and the occurrence time of a preset fault keyword based on the historical use data, wherein the preset fault keyword is used for representing the abnormal state of the target engineering machinery;
the second processing module is used for performing collaborative filtering on the historical operating data based on the occurrence time of a preset fault keyword in the historical using data to determine an abnormal operating parameter;
the third processing module is used for predicting fault prediction data of the target engineering machinery in the next operation period based on the occurrence frequency, the occurrence time and the abnormal operation parameters of preset fault keywords;
and the fourth processing module is used for carrying out fault early warning based on the fault prediction data.
8. A work machine, comprising:
a memory and a processor communicatively coupled to each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the method of any of claims 1-6.
9. The work machine of claim 8, wherein the work machine is an excavator.
10. A computer-readable storage medium having stored thereon computer instructions for causing a computer to perform the method of any one of claims 1-6.
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