Fault identification method, device and equipment of hydraulic brake system and medium

Abstract

The invention provides a fault identification method and device for a hydraulic braking system, equipment and a medium, and the method comprises the steps: monitoring the hydraulic braking system through a plurality of sensors, so as to obtain time sequence data corresponding to any sensor in a target time period; performing feature extraction on the time sequence data corresponding to each sensor to obtain target features; and based on the target features, a target fault recognition model is adopted to perform fault recognition on the hydraulic braking system, and a target fault type to which the hydraulic braking system belongs is obtained. Therefore, the fault of the hydraulic braking system can be automatically recognized, and the accuracy and effectiveness of fault recognition of the hydraulic braking system are improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a fault identification method, device, equipment and medium for a hydraulic brake system. Background Art

[0002] With the rapid development of new energy vehicle technology, the Electronic Hydraulic Braking System (EHB), as a representative of the new generation of braking technology, is gradually becoming the darling of the market. EHB not only inherits the advantages of the traditional hydraulic braking system, but also integrates advanced electronic technology to achieve precise control and rapid response of braking force, greatly improving the safety and comfort of the vehicle. However, with the increase in system complexity, fault diagnosis of hydraulic brake system has become a problem that needs to be solved urgently. Summary of the invention

[0003] The present application provides a fault identification method, device, equipment and medium for a hydraulic brake system, so as to solve one of the technical problems in the related art at least to a certain extent. The technical solution of the present application is as follows:

[0004] According to one aspect of the present application, a fault identification method for a hydraulic brake system is provided, comprising:

[0005] Monitoring the hydraulic brake system through multiple sensors to obtain time series data corresponding to any of the sensors in a target time period;

[0006] Extracting features from the time series data corresponding to each of the sensors to obtain target features;

[0007] Based on the target feature, a target fault identification model is used to perform fault identification on the hydraulic brake system to obtain a target fault type to which the hydraulic brake system belongs.

[0008] According to another aspect of the present application, another fault identification device for a hydraulic brake system is provided, comprising:

[0009] A monitoring module, used to monitor the hydraulic brake system through multiple sensors to obtain time series data corresponding to any of the sensors in a target time period;

[0010] An extraction module, used for extracting features from the time series data corresponding to each of the sensors to obtain target features;

[0011] The identification module is used to perform fault identification on the hydraulic brake system based on the target feature and adopt a target fault identification model to obtain a target fault type to which the hydraulic brake system belongs.

[0012] According to another aspect of the present application, an electronic device is provided, characterized in that it includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the program, the fault identification method of the hydraulic brake system proposed in the above aspect of the present application is implemented.

[0013] According to another aspect of the present application, a non-transitory computer-readable storage medium of computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the fault identification method of the hydraulic brake system proposed in the above aspect of the present application.

[0014] According to another aspect of the present application, a computer program product is provided, including a computer program, which, when executed by a processor, implements the fault identification method for the hydraulic brake system proposed in the above aspect of the present application.

[0015] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0016] The hydraulic brake system is monitored by multiple sensors to obtain the time series data corresponding to any sensor in the target time period; the time series data corresponding to each sensor is feature extracted to obtain the target feature; based on the target feature, the target fault identification model is used to identify the fault of the hydraulic brake system to obtain the target fault type of the hydraulic brake system. In this way, the faults occurring in the hydraulic brake system can be automatically identified, and the accuracy and effectiveness of the fault identification of the hydraulic brake system can be improved.

[0017] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0019] Figure 1 A schematic flow chart of a fault identification method for a hydraulic brake system provided in Embodiment 1 of the present application;

[0020] Figure 2 A flow chart of a fault identification method for a hydraulic brake system provided in Embodiment 2 of the present application;

[0021] Figure 3 A flow chart of a fault identification method for a hydraulic brake system provided in Embodiment 2 of the present application;

[0022] Figure 4 A schematic diagram of the structure of the target fault identification model provided in this application;

[0023] Figure 5 A schematic diagram of the structure of a fault identification device for a hydraulic brake system provided in Embodiment 4 of the present application;

[0024] Figure 6 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present invention is shown. DETAILED DESCRIPTION

[0025] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0026] The EHB system provides a more efficient and safe braking solution for new energy vehicles with its compact structure, flexible control, high integration and fast pressure building speed. These advantages enable the EHB system to better meet the high requirements of new energy vehicles for braking performance, while also providing drivers with a smoother and more comfortable driving experience.

[0027] However, the complexity of the EHB system also brings new challenges. Since the system contains multiple oil circuits and mechanical components, the complex coupling relationship between these components makes fault diagnosis extremely difficult. Once the hydraulic system fails, it will not only affect the braking performance, but also pose a serious threat to the driver's safety.

[0028] The following describes the fault identification method, device, equipment and medium of the hydraulic brake system of the embodiment of the present application with reference to the accompanying drawings.

[0029] Figure 1 A flow chart of a fault identification method for a hydraulic brake system provided in Embodiment 1 of the present application.

[0030] The embodiment of the present application takes the fault identification method of the hydraulic brake system as an example in which the fault identification device of the hydraulic brake system is configured. The fault identification device of the hydraulic brake system can be applied to any electronic device so that the electronic device can perform the fault identification function of the hydraulic brake system.

[0031] Among them, the electronic device can be any device with computing capabilities, such as a personal computer (PC), an industrial computer, a host computer, a mobile terminal, a server, etc. The mobile terminal can be, for example, a mobile phone, a tablet computer, a personal digital assistant, a wearable device, and other hardware devices with various operating systems, touch screens and / or display screens.

[0032] like Figure 1As shown, the fault identification method of the hydraulic brake system may include the following steps:

[0033] Step 101 : monitor the hydraulic brake system through multiple sensors to obtain time series data corresponding to any sensor in a target time period.

[0034] Among them, the target time period can be any time period during the driving process of the vehicle to which the hydraulic braking system belongs, or it can also be any time period when the vehicle to which the hydraulic braking system belongs is in a stopped state, etc., and this application does not impose any restrictions on this.

[0035] Among them, time series data, also known as time series data, is data recorded in chronological order during the target time period.

[0036] In an embodiment of the present application, multiple sensors can be used to monitor relevant components of the hydraulic braking system. For example, multiple sensors can be used to monitor the oil outlet pressure of the first chamber of the master cylinder (also known as the brake master pump, brake master cylinder) in the hydraulic braking system, the oil outlet pressure of the second chamber of the master cylinder, the oil outlet pressure of the volumetric valve of the front axle, the oil outlet pressure of the volumetric valve of the rear axle, the oil inlet pressure of the four wheel cylinders (also known as brake slave cylinders), etc.; for another example, multiple sensors can be used to monitor the oil outlet flow rate of the first chamber of the master cylinder in the hydraulic braking system, the oil outlet flow rate of the second chamber of the master cylinder, the oil outlet flow rate of the volumetric valve of the front axle, the oil outlet flow rate of the volumetric valve of the rear axle, the oil inlet flow rate of the four wheel cylinders (also known as brake slave cylinders), etc.

[0037] In the embodiment of the present application, the hydraulic brake system can be monitored in real time through multiple sensors corresponding to the vehicle to which the hydraulic brake system belongs, so that the time series data corresponding to any sensor in the target time period can be obtained.

[0038] Step 102: extract features from the time series data corresponding to each sensor to obtain target features.

[0039] In the embodiment of the present application, feature extraction may be performed on the time series data corresponding to each sensor to obtain target features.

[0040] As an example, for any sensor, statistical analysis is performed on the time series data corresponding to the sensor to obtain statistical features, which may include at least one of the maximum value, minimum value, average value, weighted average value, median, and mode; the statistical features corresponding to each time series data are fused to obtain the target feature.

[0041] Step 103 : Based on the target feature, a target fault identification model is used to identify the fault of the hydraulic brake system, and the target fault type to which the hydraulic brake system belongs is obtained.

[0042] Among them, the target fault type may be, for example, no fault, oil leakage, oil line blockage, gas presence, etc., and this application does not impose any restrictions on this.

[0043] In the embodiment of the present application, the target fault identification model can be used to identify the fault of the hydraulic brake system based on the target feature to obtain the target fault type to which the hydraulic brake system belongs. As an example, the target feature can be input into the target fault identification model to obtain the fault type output by the target fault identification model, and the fault type output by the target fault identification model is determined as the target fault type to which the hydraulic brake system belongs.

[0044] The fault identification method of the hydraulic brake system of the embodiment of the present application monitors the hydraulic brake system through multiple sensors to obtain the time series data corresponding to any sensor in the target time period; extracts the features of the time series data corresponding to each sensor to obtain the target features; based on the target features, the target fault identification model is used to identify the fault of the hydraulic brake system to obtain the target fault type to which the hydraulic brake system belongs. In this way, the faults occurring in the hydraulic brake system can be automatically identified, and the accuracy and effectiveness of the fault identification of the hydraulic brake system can be improved.

[0045] In order to clearly explain how to extract features from the time series data corresponding to each sensor in the above-mentioned embodiment of the present application to obtain target features, the present application also proposes a fault identification method for a hydraulic brake system.

[0046] Figure 2 A flow chart of a fault identification method for a hydraulic brake system provided in Embodiment 2 of the present application.

[0047] like Figure 2 As shown, based on the above embodiment of the present application, the fault identification method of the hydraulic brake system may further include the following steps:

[0048] Step 201, determining a plurality of target moments from a target time period.

[0049] It should be noted that the explanation of the target time period in step 101 is also applicable to this embodiment and will not be repeated here.

[0050] As an example, multiple target moments can be determined from the target time period based on the set step length, wherein the time interval between any two adjacent target moments is the set step length. It should be noted that the set step length can be pre-set, and the set step length can be in seconds, minutes, hours, etc. as time units, and the present application does not limit the value of the set step length. Thus, the target moments can be evenly distributed throughout the target time period.

[0051] As another example, the target moment may be a moment selected and determined when a specific event or condition occurs in a target time period, wherein the specific event may be, for example, an alarm signal issued by the hydraulic braking system, a target operation performed by the vehicle driver on the hydraulic braking system (such as emergency braking, long continuous braking, etc.), the brake disc temperature in the hydraulic braking system being higher than a set threshold, etc., and the present application does not impose any restrictions on this.

[0052] It should also be noted that the target time may also be a time selected and determined when no specific event or condition occurs during the target time period.

[0053] Step 202: for any target moment, extract the monitoring value at the target moment from each time series data in turn.

[0054] As an example, assume that the target time includes t1, t2, and t3, and the number of sensors is 4, namely sensor a, sensor b, sensor c, and sensor d. At time t1, the monitoring values ​​extracted from the time series data corresponding to sensor a, sensor b, sensor c, and sensor d are a and b respectively. 11 、b 12 、c 13 d 14 At time t2, the monitoring values ​​extracted from the time series data corresponding to sensor a, sensor b, sensor c, and sensor d are a and 21 、b 22 、c 23 d 24 At time t3, the monitoring values ​​extracted from the time series data corresponding to sensor a, sensor b, sensor c, and sensor d are a and 31 、b 32 、c 33 d 34 .

[0055] Step 203, fusing the monitoring values ​​at each target time to obtain the target feature.

[0056] In an embodiment of the present application, after the monitoring values ​​at each target moment are extracted from each time series data, the monitoring values ​​at each target moment can be fused to obtain the target feature.

[0057] Still taking the above example as an example, the monitoring values ​​at each target time in the above example are fused, and the target features obtained may be, for example:

[0058]

[0059] Where T is the transpose.

[0060] It should be noted that the above examples of obtained target features are only exemplary.

[0061] In order to accurately obtain the target features, in a possible implementation method of an embodiment of the present application, for any target moment, a one-dimensional vector corresponding to the target moment can be generated based on each monitoring value at the target moment; the one-dimensional vectors corresponding to each target moment are fused to obtain the target features.

[0062] Still using the above example to illustrate, for time t1, the one-dimensional vector corresponding to the target time is generated as (a 11 b 12 c 13 d 14 ), for time t2, the one-dimensional vector corresponding to the target time is generated as (a 21 b 22 c 23 d 24 ), for time t3, the one-dimensional vector corresponding to the target time is generated as (a 31 b 32 c 33 d 34 ), the one-dimensional vectors corresponding to the above target moments are fused to obtain the target features as shown in formula (1) or (2). For example, it can also be:

[0063]

[0064] Furthermore, in order to accurately obtain the target features, in a possible implementation of the embodiment of the present application, the one-dimensional vectors corresponding to each target moment may be arranged and combined in sequence according to the sequence of each target moment to obtain the target features.

[0065] Still using the above example to illustrate, assuming that the order of the target moments is t2, t3, and t1, then the one-dimensional vectors corresponding to the target moments are arranged and combined in sequence to obtain the target features:

[0066]

[0067] The fault identification method of the hydraulic brake system of the embodiment of the present application determines multiple target moments from the target time period; for any target moment, extracts the monitoring value at the target moment from each time series data in turn; and fuses each monitoring value at each target moment to obtain the target feature. Thus, it is possible to extract valuable information from the time series data, that is, to extract the target feature.

[0068] In order to clearly explain how to obtain the target fault identification model in any of the above embodiments of the present application, the present application also proposes a fault identification method for a hydraulic brake system.

[0069] Figure 3 This is a flow chart of the fault identification method of the hydraulic brake system provided in Example 3 of the present application.

[0070] like Figure 3 As shown, based on any of the above embodiments of the present application, the fault identification method of the hydraulic brake system may further include the following steps:

[0071] Step 301 : monitor a sample hydraulic brake system through a plurality of sample sensors to obtain sample time series data of any sample sensor in any first time period of a plurality of first time periods.

[0072] It should be noted that this application does not restrict the value of the first time period.

[0073] In order to obtain sample time series data, as an example, AMESim (Advanced Modeling Environment for performing Simulation of engineering systems), a multi-domain and multi-disciplinary system modeling and simulation tool, can be used to establish a simulation model of the EHB system, thereby obtaining a sample hydraulic brake system, and setting corresponding sample sensors in the simulation model to monitor parameters such as pressure and flow of related components. In order to construct different fault types, the equivalent hole diameter of the relevant oil port in the simulation model established based on AMESim software is changed to simulate the fault effect. For example, taking the equivalent hole diameters of the oil outlets of the first and second chambers of the master cylinder, the oil outlets of the displacement valves of the front and rear axles, and the oil inlets of the four wheel cylinders as targets, the equivalent hole diameters of all oil outlets under normal conditions are pre-set to be 0.2 mm; taking the failure of the left front wheel cylinder as an example, when the left front wheel cylinder has an oil circuit blockage failure, the equivalent hole diameters of the oil outlets of the first and second chambers of the master cylinder and the oil outlets of the displacement valves of the front and rear axles are set to be 0.2 mm, and the equivalent hole diameter of the oil inlet of the left front wheel cylinder is set to be 0.1 mm, and simulation is performed to obtain sample time series data; When an oil leakage occurs in the left front wheel cylinder, the equivalent hole diameters of the oil outlets of the first and second chambers of the master cylinder and the oil outlets of the volumetric valves of the front and rear axles are set to 0.2mm, and the equivalent hole diameter of the oil inlet of the left front wheel cylinder is 0.5mm, and this is used for simulation to obtain sample timing data; when a gas fault occurs, the equivalent hole diameters of the oil outlets of the first and second chambers of the master cylinder and the oil outlets of the volumetric valves of the front and rear axles are set to 0.1mm, and the equivalent hole diameter of the oil inlet of the left front wheel cylinder is 0.2mm, and this is used for simulation to obtain sample timing data.

[0074] Step 302: for any first time period, feature extraction is performed on the sample time series data corresponding to each sample sensor to obtain corresponding sample features.

[0075] It should be noted that the method for acquiring sample features is similar to the method for acquiring target features in step 102, and will not be described in detail here.

[0076] Step 303: divide the multiple sample features to obtain a training set and a test set.

[0077] Optionally, multiple sample features may be divided according to a set ratio to obtain a training set and a test set.

[0078] Among them, the set ratio is pre-set, and this application does not limit the value of the set ratio.

[0079] As an example, assuming that the set ratio is 3:2, multiple sample features can be divided according to the set ratio to obtain a training set and a test set.

[0080] Step 304 , training and testing the initial fault identification model based on the training set and the test set respectively, to obtain a target fault identification model.

[0081] Wherein, based on the training set, training the initial fault recognition model may include the following steps:

[0082] Step 3041, for any sample feature in the training set, obtain the labeling information of the sample feature.

[0083] The marking information may be used to indicate the marked fault type to which the sample hydraulic brake system belongs in the corresponding first time period.

[0084] Among them, the marked fault type can be, for example, no fault, oil leakage, oil line blockage, gas presence, etc., and this application does not impose any restrictions on this.

[0085] Step 3042: label the sample features using the labeling information, and based on the labeled sample features, use the initial fault identification model to identify the fault of the sample hydraulic brake system to obtain a predicted fault type.

[0086] In the embodiments of the present application, manual labeling may be used, or labeling tools, labeling models, etc. may be used to label sample features, and the present application does not impose any restrictions on this.

[0087] In the embodiment of the present application, the sample hydraulic brake system can be fault identified using the initial fault identification model based on the labeled sample features to obtain a predicted fault type. For example, the labeled sample features can be input into the initial fault identification model, and the response or output of the initial fault identification model can be used as the predicted fault type.

[0088] Step 3043: training the initial fault recognition model based on the difference between the labeled fault type corresponding to each sample feature and the corresponding predicted fault type.

[0089] As a possible implementation method, a loss function may be used to determine a loss value based on the difference between the labeled fault type corresponding to each sample feature and the corresponding predicted fault type; and the initial fault recognition model may be trained based on the loss value.

[0090] As an example, a loss function can be used to determine a loss value based on the difference between the labeled fault type corresponding to each sample feature and the corresponding predicted fault type, and the model parameters in the initial recognition model can be adjusted based on the loss value to minimize the loss value.

[0091] It should be noted that the above only uses the termination condition of model training as the minimization of the loss value as an example. In actual application, other termination conditions can also be set, such as the number of training times reaching a set number of times, the training time reaching a set time, the loss value convergence, etc. This application does not impose any restrictions on this.

[0092] Optionally, the target fault identification model may include three convolution layers, wherein the sizes of the convolution kernels in the first convolution layer and the third convolution layer are both 3×3, the size of the convolution kernel in the second convolution layer is 2×2, and the step size of any convolution layer is 11.

[0093] Optionally, the target fault identification model may further include two pooling layers, wherein the size of the pooling window of the first pooling layer is 3×3, the size of the pooling window of the second pooling layer is 2×2, and the step size of any pooling layer is 11.

[0094] As an example, Figure 4 The structure of the target fault identification model is shown as follows: Figure 4As shown, the data output layer is used to obtain the target features, and after normalizing the target features, the data is input into the first convolution layer, and after the convolution operation of the first convolution layer, the filtering process of the first ReLU excitation layer, the maximum pooling process of the first pooling layer, the convolution operation of the second convolution layer, the filtering process of the second ReLU excitation layer, the maximum pooling process of the second pooling layer, the convolution operation of the third convolution layer, the filtering process of the third ReLU excitation layer, the feature integration of the fully connected layer, and the calculation of the normalized exponential function softmax function, the probability of various types of faults occurring in the hydraulic brake system is obtained, and according to the probability of various types of faults occurring in the hydraulic brake system, the target fault type to which the hydraulic brake system fault belongs is output. It should be noted that in order to reduce the amount of calculation of the fully connected layer and prevent the occurrence of overfitting, dropout, regularization and other technologies can be used to reduce the number of parameters of the fully connected layer.

[0095] The fault identification method of the hydraulic brake system of the embodiment of the present application monitors the sample hydraulic brake system through multiple sample sensors to obtain the sample time series data of any sample sensor in any first time period of multiple first time periods; for any first time period, feature extraction is performed on the sample time series data corresponding to each sample sensor to obtain the corresponding sample feature; multiple sample features are divided to obtain a training set and a test set; based on the training set and the test set, the initial fault identification model is trained and tested to obtain the target fault identification model. Therefore, by constructing the training set and the test set, the initial fault identification model is trained and tested in turn, and the target identification model is obtained.

[0096] With the above Figures 1 to 3 Corresponding to the fault identification method of the hydraulic brake system provided in the embodiment, the present application also provides a fault identification device for the hydraulic brake system. Figures 1 to 3 The fault identification method of the hydraulic brake system provided in the embodiment corresponds to the embodiment, so the implementation method of the fault identification method of the hydraulic brake system is also applicable to the fault identification device of the hydraulic brake system provided in the embodiment of the present application, and will not be described in detail in the embodiment of the present application.

[0097] Figure 5 This is a schematic diagram of the structure of the fault identification device of the hydraulic brake system provided in Example 4 of the present application.

[0098] like Figure 5 As shown, the fault identification device 500 of the hydraulic brake system may include: a monitoring module 501 , an extraction module 502 and an identification module 503 .

[0099] The monitoring module 501 is used to monitor the hydraulic brake system through multiple sensors to obtain the time series data corresponding to any sensor in the target time period.

[0100] The extraction module 502 is used to extract features from the time series data corresponding to each sensor to obtain target features.

[0101] The identification module 503 is used to perform fault identification on the hydraulic brake system based on the target feature and adopt a target fault identification model to obtain the target fault type of the hydraulic brake system.

[0102] In a possible implementation of an embodiment of the present application, the extraction module 502 is used to: determine multiple target moments from a target time period; for any target moment, extract the monitoring value at the target moment from each time series data in turn; and fuse each monitoring value at each target moment to obtain the target feature.

[0103] In a possible implementation of an embodiment of the present application, the extraction module 502 is used to: for any target moment, based on each monitoring value at the target moment, generate a one-dimensional vector corresponding to the target moment; and fuse the one-dimensional vectors corresponding to each target moment to obtain the target feature.

[0104] In a possible implementation of the embodiment of the present application, the extraction module 502 is used to: arrange and combine the one-dimensional vectors corresponding to each target moment in sequence according to the sequence of each target moment to obtain the target feature.

[0105] In a possible implementation of the embodiment of the present application, the fault identification device 500 of the hydraulic brake system may also include an acquisition module, which is used to: monitor the sample hydraulic brake system through multiple sample sensors to obtain sample timing data of any sample sensor in any first time period of multiple first time periods; for any first time period, perform feature extraction on the sample timing data corresponding to each sample sensor to obtain corresponding sample features; divide the multiple sample features to obtain a training set and a test set; and train and test the initial fault identification model based on the training set and the test set, respectively, to obtain a target fault identification model.

[0106] In a possible implementation of an embodiment of the present application, an acquisition module is used to: obtain labeling information of a sample feature for any sample feature in a training set; wherein the labeling information is used to indicate a labeled fault type to which the sample hydraulic brake system belongs in a corresponding first time period; label the sample feature using the labeling information, and based on the labeled sample feature, use an initial fault identification model to identify faults in the sample hydraulic brake system to obtain a predicted fault type; and train the initial fault identification model based on the difference between the labeled fault type corresponding to each sample feature and the corresponding predicted fault type.

[0107] In a possible implementation of the embodiment of the present application, the fault identification device 500 of the hydraulic brake system may further include:

[0108] The processing module is used to preprocess the target features, wherein the preprocessing includes at least one of normalization and missing value filling.

[0109] The fault identification device of the hydraulic brake system of the embodiment of the present application monitors the hydraulic brake system through multiple sensors to obtain the time series data corresponding to any sensor in the target time period; extracts features from the time series data corresponding to each sensor to obtain target features; based on the target features, a target fault identification model is used to identify the fault of the hydraulic brake system to obtain the target fault type to which the hydraulic brake system belongs. In this way, automatic identification of faults occurring in the hydraulic brake system can be achieved, thereby improving the accuracy and effectiveness of fault identification of the hydraulic brake system.

[0110] In order to implement the above embodiments, the present application also proposes an electronic device, characterized in that it includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the fault identification method of the hydraulic brake system proposed in any of the aforementioned embodiments of the present application is implemented.

[0111] In order to implement the above embodiments, the present application also proposes a non-temporary computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the fault identification method of the hydraulic brake system proposed in any of the above embodiments of the present application is implemented.

[0112] In order to implement the above embodiments, the present application also provides a computer program product. When the instructions in the computer program product are executed by a processor, the fault identification method of the hydraulic brake system proposed in any of the above embodiments of the present application is executed.

[0113] like Figure 6As shown, the electronic device 12 is in the form of a general purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0114] The bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnection (PCI) bus.

[0115] The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0116] The memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 6 not shown, usually called a "hard drive"). Although Figure 6Not shown, a disk drive for reading and writing a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a compact disc read-only memory (CD-ROM), a digital versatile disc read-only memory (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application.

[0117] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0118] The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 12, and / or may communicate with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the electronic device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via a bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0119] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the methods mentioned in the above embodiments.

[0120] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0121] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0122] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0123] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0124] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0125] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0126] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0127] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A fault identification method for a hydraulic brake system, characterized in that: The method comprises: Monitoring the hydraulic brake system through multiple sensors to obtain time series data corresponding to any of the sensors in a target time period; Extracting features from the time series data corresponding to each of the sensors to obtain target features; Based on the target feature, a target fault identification model is used to perform fault identification on the hydraulic brake system to obtain a target fault type to which the hydraulic brake system belongs.

2. The method according to claim 1, characterized in that The extracting features of the time series data corresponding to each of the sensors to obtain target features includes: determining a plurality of target moments from the target time period; For any of the target moments, extracting the monitoring values ​​at the target moments from each of the time series data in sequence; The monitoring values ​​at each target moment are fused to obtain the target feature.

3. The method according to claim 2, characterized in that The fusing of the monitoring values ​​at the target moments to obtain the target features includes: For any of the target moments, based on the monitoring values ​​at the target moments, generate a one-dimensional vector corresponding to the target moment; The one-dimensional vectors corresponding to each target moment are fused to obtain the target feature.

4. The method according to claim 3, characterized in that The step of fusing the one-dimensional vectors corresponding to the target moments to obtain the target features includes: According to the sequence of the target moments, the one-dimensional vectors corresponding to the target moments are arranged and combined in sequence to obtain the target features.

5. The method according to any one of claims 1 to 4, characterized in that: The acquisition of the target fault identification model includes: Monitoring the sample hydraulic brake system by using a plurality of sample sensors to obtain sample time series data of any of the sample sensors in any of a plurality of first time periods; For any of the first time periods, feature extraction is performed on the sample time series data corresponding to each of the sample sensors to obtain corresponding sample features; Dividing the plurality of sample features to obtain a training set and a test set; Based on the training set and the test set, the initial fault identification model is trained and tested respectively to obtain the target fault identification model.

6. The method according to claim 5, characterized in that The training of the initial fault identification model includes: For any of the sample features in the training set, obtain the labeling information of the sample feature; wherein the labeling information is used to indicate the labeling fault type to which the sample hydraulic brake system belongs in the corresponding first time period; The sample features are labeled using the labeling information, and based on the labeled sample features, an initial fault identification model is used to identify faults of the sample hydraulic brake system to obtain a predicted fault type; The initial fault recognition model is trained based on the difference between the labeled fault type corresponding to each of the sample features and the corresponding predicted fault type.

7. The method according to any one of claims 1 to 4, characterized in that: Before performing fault identification on the hydraulic brake system based on the target feature and using a target fault identification model to obtain a target fault type to which the hydraulic brake system belongs, the method further includes: The target feature is preprocessed, wherein the preprocessing includes at least one of normalization and missing value filling.

8. A fault identification device for a hydraulic brake system, characterized in that: The device comprises: A monitoring module, used to monitor the hydraulic brake system through multiple sensors to obtain time series data corresponding to any of the sensors in a target time period; An extraction module, used for extracting features from the time series data corresponding to each of the sensors to obtain target features; The identification module is used to perform fault identification on the hydraulic brake system based on the target feature and adopt a target fault identification model to obtain a target fault type to which the hydraulic brake system belongs.

9. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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