CN111140611B - Brake friction plate wear prediction method and device, vehicle and storage medium - Google Patents
Brake friction plate wear prediction method and device, vehicle and storage medium Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16D—COUPLINGS FOR TRANSMITTING ROTATION; CLUTCHES; BRAKES
- F16D66/00—Arrangements for monitoring working conditions, e.g. wear, temperature
- F16D66/02—Apparatus for indicating wear
- F16D66/021—Apparatus for indicating wear using electrical detection or indication means
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
- F16D—COUPLINGS FOR TRANSMITTING ROTATION; CLUTCHES; BRAKES
- F16D66/00—Arrangements for monitoring working conditions, e.g. wear, temperature
- F16D2066/003—Position, angle or speed
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Abstract
The invention discloses a brake friction plate wear prediction method and device, a vehicle and a storage medium. The method comprises the following steps: according to service braking data of a target vehicle, establishing a brake use model of the target vehicle, wherein the brake use model comprises current use habit parameters of the target vehicle, and the current use habit parameters comprise current braking frequency, current braking duration and a current braking intensity curve; comparing the brake use model with a pre-established wear prediction model; and predicting the abrasion resistance of the brake friction plate of the target vehicle according to the comparison result. According to the technical scheme, the safety and reliability of the use of the brake friction plate are improved by predicting the abrasion resistance.
Description
Technical Field
The embodiment of the invention relates to the technical field of vehicle braking, in particular to a brake friction plate wear prediction method and device, a vehicle and a storage medium.
Background
With the development of domestic economy and the increasing maturity of highway freight markets, the requirements on the reliability, safety and intellectualization of commercial vehicles are higher and higher. The vehicle often needs to slow down or brake during the driving process, and the braking performance directly influences the driving safety and is greatly concerned.
Taking a commercial vehicle as an example, the current mainstream brake is a drum brake, and braking is realized through resistance generated by a friction plate. The friction plate has high use frequency, needs manual regular inspection and replacement and is complex to operate; although some vehicles are provided with the wear indicators, the wear indicators are worn off, that is, after the friction plates are worn, the conducting wires of the wear indicators are worn off, and then an alarm is given, so that the potential safety hazard is brought to the driving process because the friction plates cannot be replaced in time before being worn off. Therefore, the prior art cannot automatically predict the abrasion resistance of the friction plate in the brake before the friction plate is broken, and the problems of low safety and poor reliability exist in the use process of the friction plate.
Disclosure of Invention
The invention provides a brake friction plate wear prediction method, a brake friction plate wear prediction device, a vehicle and a storage medium, and aims to improve the safety and reliability of the use of the brake friction plate.
In a first aspect, an embodiment of the present invention provides a brake friction plate wear prediction method, including:
according to service braking data of a target vehicle, establishing a brake use model of the target vehicle, wherein the brake use model comprises current use habit parameters of the target vehicle, and the current use habit parameters comprise current braking frequency, current braking duration and a current braking intensity curve;
comparing the brake use model with a pre-established wear prediction model;
and predicting the abrasion resistance of the brake friction plate of the target vehicle according to the comparison result.
Further, before the establishing a brake usage model of the target vehicle, the method further includes:
acquiring vehicle data, wherein the vehicle data comprises vehicle type data, friction plate replacement data, vehicle running data and Controller Area Network (CAN) signal data;
dividing the vehicle data into preset categories according to the vehicle type data and the vehicle driving data;
and respectively counting the braking frequency, the driving mileage and the driving time corresponding to each preset category, respectively calculating the braking intensity curve corresponding to each preset category, and generating the wear prediction model.
Further, the predicting the abrasion resistance of the brake friction plate of the target vehicle according to the comparison result includes:
determining a preset category to which the brake use model belongs;
determining the driving mileage or the driving time matched with the current braking frequency, the current braking duration and the current braking intensity curve in the preset category;
and predicting the anti-wear capability of the target vehicle according to the driving mileage or the driving time, wherein the anti-wear capability comprises the remaining driving mileage, the remaining driving time or the friction plate replacement time.
Further, the establishing a brake usage model of the target vehicle according to service brake data of the target vehicle includes:
after a braking signal is received, calculating the current braking intensity and wear rate corresponding to each time period according to the displacement sensor data and the vehicle speed data of the target vehicle during braking according to a set period, and generating the brake use model by combining the current braking frequency, the current braking duration, the positioning data and the vehicle type of the target vehicle.
Further, the current braking intensity is the ratio of the speed difference between the starting vehicle speed and the ending vehicle speed in the corresponding time period to a set period;
the wear rate includes a first wear rate based on mileage and a second wear rate based on time; wherein,
the first wear rate is the ratio of the first parameter to the driving mileage in the corresponding time period;
the second wear rate is the ratio of the first parameter to the travel time in the corresponding time period;
the first parameter is determined according to the maximum value of the preprocessed displacement sensor data in the corresponding time interval, the radius of the tire, the load value, the current braking strength, preset parameters representing the relation between the braking torque and the displacement and the adaptability coefficient of the friction plate.
Further, the method also comprises the following steps:
comparing the wear rate and displacement sensor data during braking with a first threshold range and a second threshold range, respectively;
and if the wear rate exceeds the first threshold range or the data of the displacement sensor exceeds the second threshold range, generating prompt information of brake failure.
Further, the displacement sensor is a push rod type displacement sensor;
the displacement sensor is arranged on one side of a leading shoe in the advancing direction of a brake of a target vehicle, a bracket of the displacement sensor shares a bolt with a bracket mounting hole of the brake, and a push rod end of the displacement sensor is kept in contact with a shoe plate in the brake.
In a second aspect, an embodiment of the present invention provides a brake friction plate wear prediction device, including:
the system comprises a first modeling module, a second modeling module and a third modeling module, wherein the first modeling module is used for establishing a brake use model of a target vehicle according to service braking data of the target vehicle, the brake use model comprises current use habit parameters of the target vehicle, and the current use habit parameters comprise current braking frequency, current braking duration and a current braking intensity curve;
the comparison module is used for comparing the brake use model with a pre-established wear prediction model;
and the prediction module is used for predicting the abrasion resistance of the brake friction plate of the target vehicle according to the comparison result.
In a third aspect, an embodiment of the present invention provides a vehicle, including:
a brake;
the displacement sensor is used for measuring the displacement of a friction plate in the brake;
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the brake friction plate wear prediction method of the first aspect.
In a fourth aspect, the present invention further provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the brake friction plate wear prediction method according to the first aspect.
The embodiment of the invention provides a brake friction plate wear prediction method and device, a vehicle and a storage medium. The method comprises the following steps: according to service braking data of a target vehicle, establishing a brake use model of the target vehicle, wherein the brake use model comprises current use habit parameters of the target vehicle, and the current use habit parameters comprise current braking frequency, current braking duration and a current braking intensity curve; comparing the brake use model with a pre-established wear prediction model; and predicting the abrasion resistance of the brake friction plate of the target vehicle according to the comparison result. Through above-mentioned technical scheme, improve the fail safe nature that the stopper friction disc used.
Drawings
FIG. 1 is a flow chart of a method for predicting wear of a friction plate of a brake according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a method for predicting wear of a friction plate of a brake according to a second embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating an implementation of a brake friction plate wear prediction method according to a second embodiment of the present invention;
fig. 4 is a schematic diagram of the arrangement position of the brake in the target vehicle in the second embodiment of the invention;
fig. 5 is a schematic structural diagram of a brake friction plate wear prediction device according to a third embodiment of the present invention;
fig. 6 is a schematic hardware structure diagram of a vehicle according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. In addition, the embodiments and features of the embodiments in the present invention may be combined with each other without conflict. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a brake friction plate wear prediction method according to an embodiment of the present invention, which is applicable to a case where wear resistance of a friction plate in a vehicle brake is predicted. Specifically, the brake lining wear prediction method may be executed by a brake lining wear prediction device, which may be implemented in software and/or hardware and integrated in a vehicle. As shown in fig. 1, the method specifically includes the following steps:
s110, establishing a brake use model of the target vehicle according to service brake data of the target vehicle, wherein the brake use model comprises current use habit parameters of the target vehicle, and the current use habit parameters comprise current brake frequency, current brake duration and a current brake intensity curve.
Specifically, the service braking data comprises displacement sensor data and CAN signal data, wherein the displacement sensor data CAN be obtained by measuring the displacement of a friction plate in the brake during the braking period through a push rod type displacement sensor, and the CAN signal data comprises GPS data, braking signals, vehicle speed, load and the like and is used for counting the braking frequency and calculating the braking strength under various working conditions. In this embodiment, a brake use model of the target vehicle is established according to the service braking data, where the brake use model includes current use habit parameters of the target vehicle, that is, a current braking intensity curve (a curve formed by braking intensities in a preset time period before wear prediction is performed and used for reflecting a variation trend of the braking intensity of the vehicle in the time period), a current braking duration, and a current braking frequency under a current working condition, and the current braking frequency may be obtained through statistics in an adjacent preset time period. In the process of establishing a brake use model according to service brake data, whether the data of the displacement sensor and the data of theoretical calculation are reasonable or not needs to be judged, and the influence of parameter change (such as clearance adjustment) on modeling under the normal running working condition is eliminated.
And S120, comparing the brake use model with a pre-established wear prediction model.
Specifically, the wear prediction model is pre-established based on a large amount of vehicle data in a database. A large amount of vehicle data are extracted from a database and used as sample data or empirical data, and statistical analysis is carried out on the vehicle data from multiple dimensions such as driving areas, seasons, road conditions, braking habits (braking frequency, braking intensity, vehicle speed and gears), vehicle types and the like, so that a wear prediction model is established and is used for being matched with the working condition of a target vehicle to obtain a corresponding wear prediction result. For example, in the wear prediction model, the more serious the wear degree of the vehicle, which is higher in braking frequency, poorer in road condition environment, congested road section, higher in vehicle speed or higher in braking strength, the lower the corresponding wear resistance, and the higher the replacement frequency of the friction plate; the braking frequency is lower, the road condition environment is better, the smooth highway section of going, the vehicle degree of wear that the speed of a motor vehicle is slower or brake intensity is less relatively, and the ability of resisting wear that corresponds is higher, and the friction disc change frequency is lower.
And S130, predicting the abrasion resistance of the brake friction plate of the target vehicle according to the comparison result.
Specifically, a brake use model of the target vehicle is subjected to multi-dimensional decomposition, the brake use model is respectively compared with a wear prediction model in multiple dimensions, and the wear resistance corresponding to a working condition matched with a current braking intensity curve, a current braking duration and a current braking frequency in the wear prediction model is determined and used as the wear resistance of the target vehicle, namely, the residual travel mileage or the residual travel time is predicted by a Bayesian estimation method, and when to replace a friction plate is determined. In this embodiment, the wear resistance reflects the service life of the brake disk, and the predicted wear resistance may be embodied as predicted remaining mileage, remaining travel time, or predicted replacement time of the disk, or the like.
According to the brake friction plate wear prediction method provided by the embodiment of the invention, the brake use model and the wear prediction model of the target vehicle are compared, the wear resistance of the target vehicle under the current working condition is determined on the basis of experience provided by a large amount of vehicle data, regular manual detection is not needed, a user can conveniently and reasonably arrange the vehicle to go out, the friction plates in the brake are replaced in time, and the wear detection efficiency and the safety and reliability of the use of the friction plates are improved; particularly for commercial vehicles adopting drum brakes, due to frequent braking, the friction plates are seriously worn, the abrasion resistance is poor, and the replacement frequency of the friction plates is high.
Example two
Fig. 2 is a flowchart of a brake friction plate wear prediction method according to a second embodiment of the present invention. The embodiment is optimized on the basis of the above embodiment, and specifically describes a process of establishing a wear prediction model and a brake use model and a comparison process of the two models. It should be noted that technical details that are not described in detail in the present embodiment may be referred to any of the above embodiments.
Specifically, as shown in fig. 2, the method specifically includes the following steps:
s210, vehicle data are obtained, wherein the vehicle data comprise vehicle type data, friction plate replacement data, vehicle running data and Controller Area Network (CAN) signal data.
Specifically, a large amount of vehicle data is extracted from a database as sample data, and the vehicle data comprises vehicle type data, friction plate replacement data, vehicle running data and CAN signal data, wherein the vehicle type data refers to vehicle characteristics such as vehicle type, brand, load, displacement and the like, and different vehicle types have different abrasion resistance on brake friction plates during braking; the friction plate replacement data can be replacement time, replacement frequency and the like of the friction plate; the vehicle driving data refers to the driving area, season, driving road condition and the like of the vehicle, and the abrasion resistance of the brake is different when the brake is applied under the conditions of different road conditions, different seasons and the like; the CAN signal data comprises a braking signal, a vehicle speed, gears, a wheel speed and the like and is used for reflecting the braking intensity and the braking frequency.
And S220, dividing the vehicle data into preset categories according to the vehicle type data and the vehicle running data.
Specifically, the vehicle data is divided into preset categories according to road conditions, seasons, vehicle types and the like, for example, the categories of sedans, spring and driving on flat roads; large trucks, winter, classes driving on steep or uneven roads, etc.
And S230, respectively counting the braking frequency, the braking duration, the driving mileage and the driving time corresponding to each preset category, respectively calculating the braking intensity curve corresponding to each preset category, and generating the wear prediction model.
Specifically, vehicle data are divided into preset categories and are subjected to classified statistics, and the braking strength of each braking under different categories can be calculated according to braking signals, vehicle speed, gears and the like; according to the braking signals, the vehicle type data and the vehicle running data, the braking frequency and the braking duration under different categories can be classified and counted; according to the replacement data of the friction plates, the driving mileage or the driving time and the like under different categories can be counted in a classified mode. For example, for a small car, spring and the class of running on a flat road, the braking frequency is low, the abrasion degree is low, the replacement frequency of the friction plate is low, and the service life is long, such as 10 kilometers on average or once in three years; for large trucks, winter and the class of running on steep or uneven roads, the braking frequency is high, the abrasion degree is high, the replacement frequency of the friction plate is high, and the service life is short, such as 5 kilometers or once a year. And establishing a wear prediction model on the basis, and storing the wear prediction model in a local or network database for comparison with a brake application model of the target vehicle.
S240, after the braking signal is received, according to the displacement sensor data and the vehicle speed data of the target vehicle during braking, the current braking strength and the wear rate corresponding to each time period are calculated according to a set period, and a brake use model is generated by combining the current braking frequency, the current braking duration, the positioning data and the vehicle type of the target vehicle.
Specifically, after a brake signal is received, displacement sensor data and CAN signal data are acquired according to a set period (for example, every 10ms), the brake strength of every 10ms and the theoretical displacement of a displacement sensor are calculated according to the vehicle speed, the wheel speed or the load in the CAN signal data, the real-time wear rate of a brake friction plate CAN be calculated based on the driving mileage or the driving time by combining the actual displacement data, the theoretical displacement data, friction plate material parameters, a fatigue test empirical formula and the like, and a brake application model is generated by combining the current brake frequency, the current brake duration, positioning data (GPS data for determining the driving area, the road condition and the like of a target vehicle) and the vehicle type.
And S250, comparing the brake use model with a pre-established wear prediction model.
And S260, determining a preset category to which the brake use model belongs.
And S270, determining the driving mileage or the driving time matched with the current braking frequency, the current braking duration and the current braking intensity curve in the preset category to which the vehicle belongs.
Specifically, according to the current braking frequency, the current braking duration, the positioning data, the vehicle type, the calculated current braking strength curve and the like in the brake use model, the current working condition of the target vehicle can be determined to be consistent with which preset type in the wear prediction model, the driving mileage or the driving time which can be used by the brake under the condition of the preset type can be obtained, and the replacement time of the friction plate is further determined.
And S280, predicting the anti-wear capacity of the target vehicle according to the driving mileage or the driving time, wherein the anti-wear capacity comprises the remaining driving mileage, the remaining driving time or the friction plate replacement time.
Specifically, under the condition of the preset category, the remaining driving mileage or the remaining driving time can be obtained by subtracting the form mileage or the form time that the target vehicle has currently driven from the total driving mileage or the driving time that the friction plate can use, and the replacement time of the friction plate can also be determined. In the present embodiment, the wear resistance is reflected by the remaining mileage, the remaining running time, or the friction plate replacement time, etc., for example, the lower the wear resistance, the less the remaining mileage or the remaining running time; the higher the abrasion resistance, the more mileage or time remaining.
Further, the current braking strength is the ratio of the speed difference between the initial vehicle speed and the final vehicle speed in the corresponding time period to the set period; the wear rate includes a first wear rate based on mileage and a second wear rate based on time; wherein the first wear rate is a ratio of the first parameter to the driving mileage in the corresponding time period; the second wear rate is the ratio of the first parameter to the travel time in the corresponding time period; the first parameter is determined according to the maximum value of the preprocessed displacement sensor data in the corresponding time interval, the radius of the tire, the load value, the current braking strength, the preset parameter representing the relation between the braking torque and the displacement and the adaptability coefficient of the friction plate.
Specifically, the method for calculating the current braking strength includes: z ═ V1-V2) Δ t, where Z is the real-time braking intensity, V1、V2The starting speed and the ending speed of the set calculation period are respectively, and delta t is the set period, namely 10 ms.
The method for calculating the wear rate comprises the following steps: first rate of wear
Second rate of wear
Wherein, γ1、γ2The method comprises the steps of respectively setting a first wear rate based on mileage and a second wear rate based on time, wherein L is the maximum value of displacement sensor data subjected to preprocessing (filtering, removing cusp drift and the like) in a set period, R is the radius of a tire, M is load, Z is real-time brake strength, mu is an empirical coefficient, the relationship between brake torque and sensor displacement is represented, tau is a friction plate adaptability coefficient which is an empirical value and used for correcting the influence of friction plate materials, S is driving mileage in the set period, and g is gravity acceleration.
Further, the method also comprises the following steps: comparing the wear rate and displacement sensor data during braking with a first threshold range and a second threshold range, respectively; and if the wear rate exceeds the first threshold range or the data of the displacement sensor exceeds the second threshold range, generating prompt information of brake failure.
Specifically, by setting a first threshold range and a second threshold range, comparing the displacement sensor data during each braking process with the first threshold range, and comparing the wear rate with the second threshold range, the comparison may be performed at a certain frequency, for example, each braking process is compared or every fifth braking process is compared, if any one exceeds the upper limit value or the lower limit value of the corresponding range, it may be determined that the brake is faulty, and the fault may be displayed by a combination meter in the vehicle to prompt the user. Meanwhile, the brake use model generated according to the service brake data is compared with the wear prediction model, so that the wear resistance can be predicted, the replacement time of the friction plate can be determined, and the like, and the wear resistance can be displayed through the combination instrument.
Fig. 3 is a schematic diagram illustrating an implementation of a brake friction plate wear prediction method according to a second embodiment of the present invention. As shown in fig. 3, before predicting the wear resistance, a large amount of vehicle data including vehicle type data, friction plate replacement data, vehicle driving data, and CAN signal data is first acquired from a database, the vehicle data is divided into preset categories according to driving areas, seasons, road conditions, braking strengths, etc. to generate a wear prediction model, in the process, a large amount of sample data needs to be classified and counted, the braking strength is calculated according to braking signals, vehicle speed, gears, etc., the braking frequency is classified and counted according to braking signals, vehicle information, and vehicle driving data, the driving mileage or time is counted according to the friction plate replacement data, and the wear prediction model is established based on the information. The predictive model may be configured in a dedicated processor.
Secondly, after the target vehicle receives the brake signal, displacement sensor data and CAN signal data (such as vehicle speed, wheel speed, load and the like) are collected every 10ms, the current brake strength and the current brake frequency are calculated, the wear rate is calculated by combining theoretical displacement data of the displacement sensor, material parameters of the friction plate, a fatigue test empirical formula, positioning data and the like, a historical wear rate curve is generated, a brake use model is finally generated, the brake use model is compared with a wear prediction model, and the wear resistance, the remaining form mileage or the remaining form time and the friction plate replacement time are predicted. In the process of calculating the current braking strength and the current wear rate, due to the fact that errors exist in the data of the displacement sensor, preprocessing needs to be conducted on the data, such as denoising, removing the drift of a sharp point, filtering, working condition judgment, consideration of the influence of clearance adjustment and the like, so that the errors are reduced, the precision of each parameter in a brake use model is improved, and the accuracy of judging structural faults or predicting the wear resistance is improved.
In addition, the wear rate and the displacement sensor data during braking are compared with the first threshold range and the second threshold range, respectively, and whether the brake has a structural fault currently can be judged. The information about the structural failure of the brake and the predicted wear resistance is displayed by the combination meter.
Further, the displacement sensor is a push rod type displacement sensor; the displacement sensor is arranged on one side of a leading shoe in the advancing direction of a brake of a target vehicle, a bracket of the displacement sensor shares a bolt with a bracket mounting hole of the brake, and a push rod end of the displacement sensor is kept in contact with a shoe plate in the brake.
Fig. 4 is a schematic diagram of the arrangement position of the brake in the target vehicle in the second embodiment of the invention. As shown in fig. 4. The displacement sensor bracket 1 and the brake bracket mounting hole share a bolt, so that the displacement sensor 2 is ensured to be vertical to a step plane of the shoe assembly 3 and consistent with the overall motion direction of the shoe assembly 3, and the shoe assembly 3 is internally provided with a friction plate; the displacement sensor 2 is arranged on one side of a leading shoe in the advancing direction of the drum brake, and the push rod end of the displacement sensor is in contact with the shoe assembly 3 and can extend along with the movement of the shoe assembly 3; the wires of the displacement sensor 2 can be arranged together with the wires of an Anti-lock Braking System (ABS) wheel speed sensor. The displacement sensor 2 can adopt a low-cost push rod type displacement sensor, and only the displacement measurement range is required to be ensured to be larger than 2mm, the measurement precision is larger than 0.02mm, and the appearance size meets the installation requirement.
According to the brake friction plate wear prediction method provided by the embodiment of the invention, optimization is carried out on the basis of the embodiment, and the data in the wear prediction model is divided into specific preset categories, so that the brake friction plate wear prediction for various environments and working conditions is realized, and the applicability of the brake friction plate wear prediction is improved; the calculation error is reduced by preprocessing the service braking data; through comparing the wear rate and the data of the displacement sensor with a preset threshold range and comparing the brake use model with the wear prediction model, the brake structure fault is judged in advance, the wear resistance is predicted, and the wear detection efficiency and the safety of vehicle use are improved.
EXAMPLE III
Fig. 5 is a schematic structural diagram of a brake friction plate wear prediction device according to a third embodiment of the present invention. The brake friction plate wear prediction device provided by the embodiment comprises:
the first modeling module 310 is configured to establish a brake use model of a target vehicle according to service braking data of the target vehicle, where the brake use model includes current use habit parameters of the target vehicle, and the current use habit parameters include a current braking frequency, a current braking duration, and a current braking intensity curve;
a comparison module 320 for comparing the brake usage model with a pre-established wear prediction model;
and the prediction module 330 is used for predicting the abrasion resistance of the brake friction plate of the target vehicle according to the comparison result.
According to the brake friction plate wear prediction device provided by the third embodiment of the invention, a brake use model of a target vehicle is established, and the brake use model is compared with a pre-established wear prediction model; and the abrasion resistance of the brake friction plate of the target vehicle is predicted according to the comparison result, so that the use safety and reliability of the friction plate are improved.
On the basis of the above embodiment, the method further includes: a second modeling module to:
acquiring vehicle data, wherein the vehicle data comprises vehicle type data, friction plate replacement data, vehicle running data and Controller Area Network (CAN) signal data;
dividing the vehicle data into preset categories according to the vehicle type data and the vehicle driving data;
and respectively counting the braking frequency, the driving mileage and the driving time corresponding to each preset category, respectively calculating the braking intensity curve corresponding to each preset category, and generating the wear prediction model.
Further, the prediction module 330 is specifically configured to:
determining a preset category to which the brake use model belongs;
determining the driving mileage or the driving time matched with the current braking frequency, the current braking duration and the current braking intensity curve in the preset category;
and predicting the anti-wear capability of the target vehicle according to the driving mileage or the driving time, wherein the anti-wear capability comprises the remaining driving mileage, the remaining driving time or the friction plate replacement time.
On the basis of the above embodiment, the first modeling module 310 is specifically configured to:
after a braking signal is received, calculating the current braking intensity and wear rate corresponding to each time period according to the displacement sensor data and the vehicle speed data of the target vehicle during braking according to a set period, and generating the brake use model by combining the current braking frequency, the current braking duration, the positioning data and the vehicle type of the target vehicle.
Further, the current braking intensity is the ratio of the speed difference between the starting vehicle speed and the ending vehicle speed in the corresponding time period to a set period;
the wear rate includes a first wear rate based on mileage and a second wear rate based on time; wherein,
the first wear rate is the ratio of the first parameter to the driving mileage in the corresponding time period;
the second wear rate is the ratio of the first parameter to the travel time in the corresponding time period;
the first parameter is determined according to the maximum value of the preprocessed displacement sensor data in the corresponding time interval, the radius of the tire, the load value, the current braking strength, preset parameters representing the relation between the braking torque and the displacement and the adaptability coefficient of the friction plate.
Further, the method also comprises the following steps: a fault prompt module for:
comparing the wear rate and displacement sensor data during braking with a first threshold range and a second threshold range, respectively;
and if the wear rate exceeds the first threshold range or the data of the displacement sensor exceeds the second threshold range, generating prompt information of brake failure.
Further, the displacement sensor is a push rod type displacement sensor;
the displacement sensor is arranged on one side of a leading shoe in the advancing direction of a brake of a target vehicle, a bracket of the displacement sensor shares a bolt with a bracket mounting hole of the brake, and a push rod end of the displacement sensor is kept in contact with a shoe plate in the brake.
The brake friction plate wear prediction device provided by the third embodiment of the invention can be used for executing the brake friction plate wear prediction method provided by any embodiment, and has corresponding functions and beneficial effects. Technical details that are not elaborated in this embodiment may be referred to any of the embodiments described above.
Example four
Fig. 6 is a schematic hardware structure diagram of a vehicle according to a fourth embodiment of the present invention. As shown in fig. 6, the present embodiment provides a vehicle including: processor 410, memory device 420, brake 430, and displacement sensor 440, displacement sensor 440 is used to measure the displacement of the friction plates in brake 430. The number of the processors in the vehicle may be one or more, fig. 6 illustrates one processor 410, the processor 410 and the storage device 420 in the vehicle may be connected by a bus or in other manners, and fig. 6 illustrates the connection by the bus.
The one or more programs are executed by the one or more processors 410 to cause the one or more processors to implement the brake friction pad wear prediction method of any of the embodiments described above.
The storage device 420 in the vehicle, as a computer-readable storage medium, may be used to store one or more programs, which may be software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the brake friction plate wear prediction method in the embodiment of the present invention (for example, the modules in the brake friction plate wear prediction device shown in fig. 5, including the first modeling module 310, the comparison module 320, and the prediction module 330). The processor 410 executes various functional applications and data processing of the vehicle by executing software programs, instructions and modules stored in the storage device 420, namely, implements the brake friction plate wear prediction method in the above method embodiment. The wear prediction model may also be stored in the storage 420.
The storage device 420 mainly includes a storage program area and a storage data area, wherein the storage program area can store an operating system and an application program required by at least one function; the storage data area may store data created according to the use of the vehicle, etc. (service braking data, wear prediction model, etc. as in the above-described embodiments). Further, the storage 420 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the storage device 420 may further include memory located remotely from the processor 410, which may be connected to the vehicle over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
And, when one or more programs included in the above-mentioned vehicle are executed by the one or more processors 410, the following operations are performed: according to service braking data of a target vehicle, establishing a brake use model of the target vehicle, wherein the brake use model comprises current use habit parameters of the target vehicle, and the current use habit parameters comprise current braking frequency, current braking duration and a current braking intensity curve; comparing the brake use model with a pre-established wear prediction model; and predicting the abrasion resistance of the brake friction plate of the target vehicle according to the comparison result.
The vehicle proposed by the embodiment belongs to the same inventive concept as the brake friction plate wear prediction method proposed by the above embodiment, and technical details which are not described in detail in the embodiment can be referred to any of the above embodiments, and the embodiment has the same beneficial effects as the execution of the brake friction plate wear prediction method.
On the basis of the above-described embodiments, the present embodiment also provides a computer-readable storage medium having stored thereon a computer program that, when executed by a brake friction plate wear prediction apparatus, implements a brake friction plate wear prediction method in any of the above-described embodiments of the present invention, the method including: according to service braking data of a target vehicle, establishing a brake use model of the target vehicle, wherein the brake use model comprises current use habit parameters of the target vehicle, and the current use habit parameters comprise current braking frequency, current braking duration and a current braking intensity curve; comparing the brake use model with a pre-established wear prediction model; and predicting the abrasion resistance of the brake friction plate of the target vehicle according to the comparison result.
Of course, the storage medium provided by the embodiments of the present invention contains computer executable instructions, and the computer executable instructions are not limited to the operation of the brake friction plate wear prediction method described above, and may also perform related operations in the brake friction plate wear prediction method provided by any embodiment of the present invention, and have corresponding functions and advantages.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the brake pad wear prediction method according to the embodiments of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A brake friction plate wear prediction method, comprising:
according to service braking data of a target vehicle, establishing a brake use model of the target vehicle, wherein the brake use model comprises current use habit parameters of the target vehicle, and the current use habit parameters comprise current braking frequency, current braking duration and a current braking intensity curve;
comparing the brake use model with a pre-established wear prediction model;
and predicting the abrasion resistance of the brake friction plate of the target vehicle according to the comparison result.
2. The method of claim 1, prior to establishing the brake usage model of the target vehicle, comprising:
acquiring preset quantity of vehicle data, wherein the vehicle data comprises vehicle type data, friction plate replacement data, vehicle running data and Controller Area Network (CAN) signal data;
dividing the vehicle data into preset categories according to the vehicle type data and the vehicle driving data;
and respectively counting the braking frequency, the braking duration, the driving mileage and the driving time corresponding to each preset category, respectively calculating a braking intensity curve corresponding to each preset category, and generating the wear prediction model.
3. The method of claim 2, wherein the predicting the wear resistance of the brake pads of the target vehicle based on the comparison comprises:
determining a preset category to which the brake use model belongs;
determining the driving mileage or the driving time matched with the current braking frequency, the current braking duration and the current braking intensity curve in the preset category;
and predicting the anti-wear capability of the target vehicle according to the driving mileage or the driving time, wherein the anti-wear capability comprises the remaining driving mileage, the remaining driving time or the friction plate replacement time.
4. The method of claim 1, wherein the establishing a brake usage model of a target vehicle from service braking data of the target vehicle comprises:
after a braking signal is received, calculating the current braking intensity and wear rate corresponding to each time period according to the displacement sensor data and the vehicle speed data of the target vehicle during braking according to a set period, and generating the brake use model by combining the current braking frequency, the current braking duration, the positioning data and the vehicle type of the target vehicle.
5. The method according to claim 4, characterized in that the current brake intensity is a ratio of a speed difference between a starting vehicle speed and an ending vehicle speed in a corresponding time period to a set period;
the wear rate includes a first wear rate based on mileage and a second wear rate based on time; wherein,
the first wear rate is the ratio of the first parameter to the driving mileage in the corresponding time period;
the second wear rate is the ratio of the first parameter to the travel time in the corresponding time period;
the first parameter is determined according to the maximum value of the preprocessed displacement sensor data in the corresponding time period, the radius of the tire, the load value, the current braking strength, preset parameters representing the relation between the braking torque and the displacement and the adaptability coefficient of the friction plate.
6. The method of claim 4, further comprising:
comparing the wear rate and displacement sensor data during braking with a first threshold range and a second threshold range, respectively;
and if the wear rate exceeds the first threshold range or the data of the displacement sensor exceeds the second threshold range, generating prompt information of brake failure.
7. The method of any of claims 4-6, wherein the displacement sensor is a push-rod displacement sensor;
the displacement sensor is arranged on one side of a leading shoe in the advancing direction of a brake of a target vehicle, a bracket of the displacement sensor shares a bolt with a bracket mounting hole of the brake, and a push rod end of the displacement sensor is kept in contact with a shoe plate in the brake.
8. A brake friction plate wear prediction device, comprising:
the system comprises a first modeling module, a second modeling module and a third modeling module, wherein the first modeling module is used for establishing a brake use model of a target vehicle according to service braking data of the target vehicle, the brake use model comprises current use habit parameters of the target vehicle, and the current use habit parameters comprise current braking frequency, current braking duration and a current braking intensity curve;
the comparison module is used for comparing the brake use model with a pre-established wear prediction model;
and the prediction module is used for predicting the abrasion resistance of the brake friction plate of the target vehicle according to the comparison result.
9. A vehicle, characterized by comprising:
a brake;
the displacement sensor is used for measuring the displacement of a friction plate in the brake;
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the brake friction plate wear prediction method of any one of claims 1-7.
10. A computer-readable storage medium having stored thereon a computer program, wherein the program, when executed by a processor, implements a brake friction plate wear prediction method as claimed in any one of claims 1 to 7.
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| CN112697421A (en) * | 2020-12-04 | 2021-04-23 | 浙江亚太机电股份有限公司 | Self-adjusting function simulation detection equipment of drum brake |
| CN114834425A (en) * | 2021-06-04 | 2022-08-02 | 长城汽车股份有限公司 | Vehicle warning method and device and vehicle |
| CN113339430A (en) * | 2021-06-07 | 2021-09-03 | 中国重汽集团济南动力有限公司 | Vehicle brake wear state monitoring system and method |
| CN113619554B (en) * | 2021-08-20 | 2022-10-28 | 中国第一汽车股份有限公司 | Friction plate abrasion alarming method and system, vehicle and storage medium |
| CN113983092B (en) * | 2021-10-18 | 2024-04-19 | 吉林大学 | Visual monitoring method and system for working state of friction plate |
| CN114370471B (en) * | 2022-01-05 | 2023-09-01 | 东风柳州汽车有限公司 | Brake pad wear reminding method, device, equipment and storage medium |
| CN116629097B (en) * | 2023-04-17 | 2024-03-15 | 中国北方车辆研究所 | Comprehensive wear performance evaluation method for inertia load friction element |
| CN118238794A (en) * | 2024-05-28 | 2024-06-25 | 南京讯联液压技术股份有限公司 | Hydraulic brake fault detection method and system based on fuzzy neural network |
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