Motor fault detection method and system, storage medium, program product, electronic equipment and vehicle

Abstract

The invention discloses a motor fault detection method and system, a storage medium, a program product, an electronic device and a vehicle, the motor fault level is determined based on motor fault characteristic parameters, and the motor fault characteristic parameters are determined based on the acceleration of a motor in the X, Y and Z axis directions. The motor fault level can be directly determined based on the motor fault characteristic parameters, excessive occupation of processor resources is not needed, and the required hardware cost is low, so that the problems that faults cannot be quickly and accurately diagnosed and the cost is high in the prior art are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent vehicles, and in particular to a motor fault detection method and system, a storage medium, a program product, an electronic device and a vehicle. BACKGROUND

[0002] At present, when the motor fault detection is performed based on the machine learning model algorithm in the prior art, a large amount of data sets need to be processed, the processor requires high computing power, and the system cost is thus increased. Meanwhile, a large amount of processor resources are occupied, which cannot meet the demand for rapid and accurate fault diagnosis. Therefore, it is urgent to improve and innovate to meet the increasing demand of industrial production and operation. SUMMARY

[0003] The present application aims to at least solve one of the technical problems existing in the prior art. To this end, the present application provides a motor fault detection method and system, a storage medium, a program product, an electronic device and a vehicle. The motor fault level is determined based on the motor fault characteristic parameters in the present application. The motor fault characteristic parameters are determined based on the acceleration of the X, Y and Z axis directions of the motor. The motor fault level can be directly determined based on the motor fault characteristic parameters. The processor resources are not excessively occupied, and the hardware cost is low. Thus, the problems of the prior art, such as the inability to rapidly and accurately diagnose faults and high cost, are solved.

[0004] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0005] In a first aspect, the present application provides a motor fault detection method, comprising: determining a motor fault level based on a motor fault characteristic parameter, wherein the motor fault characteristic parameter is determined based on the acceleration of the X, Y and Z axis directions of the motor.

[0006] In some embodiments of the present application, the determination of the motor fault level based on the motor fault characteristic parameter comprises: determining the motor fault level based on the motor fault characteristic parameter and a set threshold value.

[0007] In some embodiments of the present application, the determination of the motor fault level based on the motor fault characteristic parameter and the set threshold value comprises: when the motor fault characteristic parameter is greater than or equal to a first threshold value and less than a second threshold value, determining the motor fault level as a first level fault; and / or when the motor fault characteristic parameter is greater than or equal to the second threshold value and less than a third threshold value, determining the motor fault level as a second level fault.

[0008] In some embodiments of the present application, the determination of the motor fault level based on the motor fault characteristic parameter and the set threshold value comprises: when the motor fault characteristic parameter is greater than or equal to the third threshold value, determining the motor fault level as a third level fault.

[0009] In some embodiments of the present application, the motor fault level is determined based on the motor fault characteristic parameter, comprising: searching for the motor fault level corresponding to the motor fault characteristic parameter from the corresponding relationship table.

[0010] In some embodiments of the present application, the method further comprises: calculating normalized data values according to the sampling values of the motor in the X-axis direction, the Y-axis direction and the Z-axis direction; and determining the motor fault characteristic parameter according to the normalized data values.

[0011] In some embodiments of the present application, the method further comprises: obtaining the acceleration of the motor in the X-axis direction, the Y-axis direction and the Z-axis direction; and determining the sampling values of the motor in the X-axis direction, the Y-axis direction and the Z-axis direction according to the acceleration.

[0012] In some embodiments of the present application, after the motor fault level is determined based on the motor fault characteristic parameter, the method further comprises: outputting prompt information when the motor is in the first fault level or the second fault level.

[0013] In some embodiments of the present application, after the motor fault level is determined based on the motor fault characteristic parameter, the method further comprises: controlling the motor to stop running when the motor is in the third fault level.

[0014] In a second aspect, the present application provides a motor fault detection system, comprising: a controller configured to determine a motor fault level based on a motor fault characteristic parameter, wherein the motor fault characteristic parameter is determined based on the acceleration of the motor in the X-axis direction, the Y-axis direction and the Z-axis direction.

[0015] In some embodiments of the present application, the system further comprises: an acceleration sensor configured to detect the acceleration of the motor in the X-axis direction, the Y-axis direction and the Z-axis direction.

[0016] In some embodiments of the present application, the system further comprises: an amplification circuit connected to the acceleration sensor and the controller, configured to output the sampling values of the motor in the X-axis direction, the Y-axis direction and the Z-axis direction based on the acceleration of the motor in the X-axis direction, the Y-axis direction and the Z-axis direction; and the controller is further configured to determine the motor fault characteristic parameter based on the sampling values of the motor in the X-axis direction, the Y-axis direction and the Z-axis direction.

[0017] In a third aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to cause a computer to implement the motor fault detection method of the first aspect.

[0018] In a fourth aspect, the present application provides a computer program product, wherein the computer program product stores instructions, and the instructions are executed by a computer to cause the computer to implement the motor fault detection method of the first aspect.

[0019] In a fifth aspect, the present application provides an electronic device, comprising: a memory, wherein a computer program is stored in the memory; and a processor, configured to execute the computer program in the memory to implement the motor fault detection method according to the first aspect.

[0020] In a sixth aspect, the present application provides a vehicle, comprising: the control system according to the second aspect; or the electronic device according to the fifth aspect; or a motor and a controller, wherein the controller is configured to execute the motor fault detection method according to the first aspect.

[0021] The vehicle, the electronic device and the control system have the same advantages as the control method, which will not be repeated here.

[0022] Additional aspects and advantages of the present application will be made apparent by the following description. BRIEF DESCRIPTION OF DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort based on these drawings.

[0024] In order to more completely understand the present application and its beneficial effects, the following will be described in conjunction with the drawings, wherein the same reference numerals in the following description represent the same parts.

[0025] Figure 1 is a structural schematic diagram of a motor fault detection system according to an embodiment of the present application;

[0026] Figure 2 is a schematic diagram of an amplification circuit according to an embodiment of the present application;

[0027] Figure 3 is a flowchart of a motor fault detection method according to an embodiment of the present application;

[0028] Figure 4 is a flowchart of a motor fault detection method according to an embodiment of the present application;

[0029] Figure 5 is a structural schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0030] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the protection scope of the present application.

[0031] In order to facilitate the understanding of the implementation scheme provided by the embodiments of the present application, the related application background of the audio processing method provided by the embodiments of the present application will be described first.

[0032] When the machine learning model algorithm is used for motor fault detection in the prior art, a large amount of data sets need to be processed, the processor requires high computing power, and the system cost is thus increased. At the same time, a large amount of processor resources are occupied, which cannot meet the demand of rapid and accurate fault diagnosis. Therefore, improvement and innovation are urgently needed to meet the increasing demand of industrial production and operation.

[0033] To solve the above problems, the present scheme is to determine the motor fault level based on the motor fault characteristic parameters, which are determined based on the acceleration of the X, Y and Z axis directions of the motor. The motor fault level can be directly determined based on the motor fault characteristic parameters, without occupying too much processor resource and with low hardware cost.

[0034] Figure 1 is a structural schematic diagram of a motor fault detection system according to some embodiments of the present specification. As shown in Figure 1 The motor fault detection system includes an acceleration sensor 101 and a controller 102, wherein: the acceleration sensor 101 is used to detect the acceleration of the X, Y and Z axis directions of the motor; and the controller 102 is used to determine the motor 103 fault level based on the motor fault characteristic parameters, which are determined based on the acceleration of the X, Y and Z axis directions of the motor.

[0035] Preferably, the above-mentioned acceleration sensor can be a three-axis acceleration sensor or a plurality of single-axis sensors, as long as it can detect the acceleration of the motor in the X, Y and Z axis directions.

[0036] As an optional implementation manner, the above-mentioned three-axis acceleration sensor can adopt a piezoresistive, piezoelectric or capacitive working principle, and the generated acceleration is proportional to the change of resistance, voltage and capacitance, which is collected through a corresponding amplification and filtering circuit. It is an electronic device for measuring the acceleration of an object in three-dimensional space, which works based on the piezoresistive, piezoelectric or capacitive principle, and realizes motion state monitoring by detecting the acceleration change of the X, Y and Z three-axis.

[0037] For example, the sensor model LIS3DHTR can be selected. LIS3DH belongs to the "nano" series of ultra-low power high-performance three-axis linear accelerometer, with digital I2C / SPI serial interface standard output. The device has an ultra-low power operating mode, which can realize advanced power saving and intelligent embedded functions.

[0038] LIS3DH has a dynamic user-selectable full scale of ±2g / ±4g / ±8g / ±16g and can measure acceleration at an output data rate of 1Hz to 5.3kHz. Self-test function allows users to check the function of the sensor in the final application. The device can be configured to generate an interrupt signal using two independent inertial wake-up / freifall events and the position of the device itself. The threshold and timing of the interrupt generator can be programmed by the end user in real time. LIS3DH has an integrated 32-level first-in-first-out (FIFO) buffer, allowing users to store data to limit host processor intervention.

[0039] Table 1 LIS3DHTR sensor parameters

[0040]

[0041]

[0042] The specific parameters of LIS3DHTR sensor are shown in Table 1. This sensor has high sensitivity and low power consumption during operation, and is suitable for detecting faults of stators and other hardware during vehicle driving. In addition, this sensor has a wide operating temperature range, which can meet the daily use conditions of vehicles.

[0043] LIS3DH is a three-axis acceleration sensor that can detect the acceleration of X, Y, and Z axes respectively, and can trigger an interrupt when the acceleration exceeds the threshold by setting the threshold. Since the permanent magnet synchronous motor can correspond to different vibration spectra when the rotor and stator hardware fails, combined with the research of other working fault vibration spectra, the X, Y, and Z axis acceleration fault thresholds can be set for different faults. By analyzing the triggered thresholds during vehicle driving, the corresponding motor hardware allowed state can be obtained.

[0044] The working process of the scheme is as follows: (1) real-time acquisition of X, Y, and Z axis accelerations by LIS3DH sensor; (2) denoising and filtering operation on the acquired three-axis acceleration; (3) comparison of the processed acceleration values to determine whether the corresponding fault is triggered; (4) loop detection.

[0045] In the above embodiment, the three-axis acceleration sensor collects data in multiple directions, which can fully reflect the vibration characteristics of the motor running state, overcome the limitations of traditional single-direction detection, and improve the accuracy of fault identification.

[0046] In some embodiments, the triaxial acceleration sensor adopts a low-power model, which is suitable for continuous monitoring under various working conditions such as vehicle static and driving. This scheme reduces sensor power consumption, prolongs system endurance, reduces hardware cost, and improves deployment flexibility.

[0047] As an optional implementation, the system further comprises an amplification circuit 104 connected to the acceleration sensor 101 at the input end and connected to the controller 102 at the output end. The amplification circuit 104 is used to output the sampling values of the motor 103 in the X, Y and Z axis directions based on the acceleration of the motor 103 in the X, Y and Z axis directions. The controller 102 is further used to determine the motor 103 fault feature parameters based on the sampling values of the motor 103 in the X, Y and Z axis directions.

[0048] Among them, the above-mentioned amplification circuit can be integrated in the sensor end, or integrated in the controller end, of course, it can also be separately arranged on the circuit board, and the present scheme does not make any limitation.

[0049] For example, the above-mentioned amplification circuit can refer to the content shown in Figure 2 , wherein: V1 is the reference voltage, V2 is the analog voltage of the acceleration collected by the sensor, and Vout is the sampling value of the output motor in the X, Y and Z axis directions. Of course, other amplification circuits can also be used in the present scheme, which are only described as examples and are not limited.

[0050] Further, the controller is further used to perform denoising and filtering processing on the sampling values output by the amplification circuit. The denoising and filtering process is realized by software, and its implementation process can refer to the prior art, which will not be described here.

[0051] The present scheme enhances the capture ability of weak signals through the signal amplification circuit, reduces environmental interference by combining denoising and filtering technology, and ensures the reliability of the feature parameters.

[0052] In addition, the present scheme collects analog signals through the above-mentioned sensor, and then uses an amplification circuit to amplify the signals to obtain sampling signals. Compared with the direct analog-digital conversion at the sensor end, the price is low and the processing is not complex, thereby further reducing the hardware cost.

[0053] Figure 3 is a flowchart of a motor fault detection method according to some embodiments of the present specification. As Figure 3 shown, the present application embodiment further provides a motor fault detection method, comprising:

[0054] 201、determining the motor fault level based on the motor fault characteristic parameter, the motor fault characteristic parameter is determined based on the acceleration of the motor in the X-axis direction, the Y-axis direction and the Z-axis direction.

[0055] As a preferred implementation, the step 201 specifically includes the following content: 201a, determining the motor fault level based on the motor fault characteristic parameter and the set threshold value.

[0056] Further preferably, the step 201a specifically includes: 201a1, when the motor fault characteristic parameter is greater than or equal to the first threshold value and less than the second threshold value, determining the motor fault level as the first level fault; and / or, 201a2, when the motor fault characteristic parameter is greater than or equal to the second threshold value and less than the third threshold value, determining the motor fault level as the second level fault.

[0057] Further optionally, based on the above 201a1 and 201a2, the above 201 specifically further includes the following content: 201a3, when the motor fault characteristic parameter is greater than or equal to the third threshold value, determining the motor fault level as the third level fault.

[0058] Among them, the severity of the second fault level is less than the first fault level and greater than the third fault level. Optionally, the first fault level can be a light fault, the second fault level can be a moderate fault, and the third fault level can be a severe fault.

[0059] Illustratively, the motor fault characteristic parameter refers to a fault characteristic value, which is usually between 0 and 1. The first threshold value, the second threshold value and the third threshold value are obtained based on experience or test, for example, they can be set to 0.3, 0.6 and 0.9 respectively. When the motor fault characteristic parameter is between [0.3, 0.6), it is determined that the motor fault level is a light fault, when the motor fault characteristic parameter is between [0.6, 0.9), it is determined that the motor fault level is a moderate fault, and when the motor fault characteristic parameter is greater than or equal to 0.9, it is determined that the motor fault level is a severe fault.

[0060] As an optional implementation, the step 201 further includes the following: 201b, searching for the motor fault level corresponding to the motor fault characteristic parameter from the corresponding relationship table. As an example, a corresponding relationship table can be preset, which includes the relationship between the motor fault characteristic parameter and the motor fault level and is stored in the controller. For example, the corresponding relationship table can include the following: when the motor fault characteristic parameter is between [a, b), the corresponding motor fault level is light fault; when the motor fault characteristic parameter is between [b, c), the corresponding motor fault level is moderate fault; and when the motor fault characteristic parameter is greater than or equal to c, the corresponding motor fault level is severe fault. The values of a, b, and c are numbers between 0 and 1, and a < b < c.

[0061] The present scheme stores the mapping relationship between the motor fault characteristic parameter and the fault level in advance, and quickly determines the fault level by searching the corresponding relationship table. This scheme does not require complex calculation, reduces the operation burden of the controller, and is particularly suitable for resource-limited embedded systems.

[0062] Further, the method further includes: 200a, acquiring the acceleration of the motor in the X-axis direction, the Y-axis direction, and the Z-axis direction; and 200b, determining the sampling value of the motor in the X-axis direction, the Y-axis direction, and the Z-axis direction according to the acceleration.

[0063] As an example, the acceleration in the step 200a can be collected by a three-axis acceleration sensor or a plurality of acceleration sensors, as long as the acceleration in the X-axis direction, the Y-axis direction, and the Z-axis direction can be obtained. The step 200b is implemented by the following method: inputting the analog voltage signal corresponding to the acceleration into V2 in the amplification circuit (such as shown in FIG. 1), and outputting the sampling value of the motor in the X-axis direction, the Y-axis direction, and the Z-axis direction by Vout in the amplification circuit. Figure 2

[0064] Further, the method further includes: 200c, calculating the normalized data value according to the sampling value of the motor in the X-axis direction, the Y-axis direction, and the Z-axis direction; and 200d, determining the motor fault characteristic parameter according to the normalized data value.

[0065] As an example, the three-axis current acceleration is set as xi, yi, and zi, and the sampling value of the motor in the X-axis direction, the Y-axis direction, and the Z-axis direction can be represented by the following set, wherein: the x-axis acceleration data set collected is x[i] = x1, x2... xn; the y-axis acceleration set is y[i] = y1, y2... yn; and the z-axis acceleration set is z[i] = z1, z2... zn.

[0066] ​Further, the specific implementation process of steps 200c and 200d is as follows: the normalized data value can be obtained by normalization processing through the following formula. The specific formula is as follows: x = (xi-xi min ) / (xi max -xi min ), and the same applies to the y-axis and the z-axis. Then, the geometric mean of the obtained motor fault characteristic parameter is represented as U.

[0067] In some embodiments, the controller calculates the normalized data value according to the sampling value of the motor in the X, Y, and Z axis directions as the basis for calculating the fault characteristic parameter. The normalization processing can eliminate the dimensional differences of different axial data, make the characteristic parameter more comparable, and improve the stability of the algorithm. For example, by using the maximum-minimum normalization method, the acceleration data of each axis is mapped to the interval [0, 1], ensuring the uniformity of the characteristic parameter.

[0068] As an optional implementation, the above method further includes: 202, outputting prompt information; the prompt information is determined based on the motor fault level.

[0069] For example, the above step 202 specifically includes the following content: 202a, in the case that the motor is in the first fault level or the second fault level, outputting prompt information.

[0070] For example, the present scheme can issue an alarm through an instrument panel, in-vehicle multimedia devices such as a sound system, or a buzzer to remind the user to check in time. This scheme avoids excessive intervention on first-level faults and second-level faults through hierarchical prompting, while ensuring timely handling of serious faults.

[0071] For example, the above method further includes: 103, in the case that the motor is in the third fault level, controlling the motor to stop running.

[0072] When the third-level fault is detected, the controller controls the motor to stop running to prevent the expansion of the fault from causing equipment damage or safety accidents. This scheme improves the safety and reliability of the system through a protection mechanism.

[0073] In the present scheme, the motor fault detection is performed from the power-on time of the vehicle, the detection function is placed in a 100ms periodic task, and the detection is started when a power-on command is obtained. The three-axis acceleration is collected by the acceleration sensor, the AD value of the sampling is read, and the three-axis acceleration value obtained by sampling is subjected to low-pass filtering processing to obtain the motor fault characteristic parameter and compare it with the fault threshold. The following is the fault level set in the software program of the present application: MtrSriFlg represents a serious motor fault, MtrMedFlg represents a moderate motor fault, and MtrSlghtFlg represents a slight motor fault.

[0074] When the motor is in slight failure or moderate failure, the driver is prompted through the dashboard, audio and other in-vehicle multimedia devices, and when the motor is in serious failure, the motor is not allowed to start.

[0075] Figure 4 is a flow chart of the fault detection according to some embodiments of the present specification. The specific operation steps are:

[0076] Step S10: After receiving the power-on command, start executing motor fault detection.

[0077] Step S20: Start motor fault detection with a period of 100ms.

[0078] Step S30: Get X, Y, Z axis acceleration.

[0079] Step S40: Filter and denoise the obtained values.

[0080] Step S50: Output the fault reference parameter and further compare it with the fault threshold.

[0081] Step S60: Perform 5-frame judgment filtering on the values stored in the array.

[0082] Step S70: According to the motor fault threshold of the detection result, set the fault flag bit, which is slight failure, moderate failure and serious failure respectively.

[0083] Step S80: Judge whether the fault is serious.

[0084] Step S90: If it is a serious failure vehicle, it is not allowed to OK.

[0085] Step S100: If it is not a serious failure, it is allowed to OK.

[0086] Step S110: According to whether there is a fault and the fault level, use the multimedia device to remind the driver.

[0087] Step S120: End.

[0088] Based on the above, the differences between the present application and the prior art are as follows:

[0089] 1) The present application is different from the prior art in the following aspects:

[0090] The application can more accurately capture the motor running state through the acceleration sensor installed on the motor to collect X, Y and Z axis acceleration, and then through the signal amplification circuit, and then the output signal after the signal amplifier is denoised and filtered to generate a motor fault detection reference value, and the generated reference value is mapped to the spectrum characteristics of each motor fault in the existing research to further confirm the potential risks of the motor, in addition, corresponding fault protection measures are set to further reduce the risk, and in addition, the acceleration sensor has low power consumption and is suitable for fault detection under various working conditions such as vehicle static and driving.

[0091] 2) The present application has two differences from the prior art:

[0092] The application has low power consumption and small loss, is suitable for detection under new energy vehicle load working conditions, has no safety hazard, and therefore, software matching can be performed on the basis, a real-time monitoring system is established, the running state of the motor is monitored and analyzed in real time, once fault signs are detected, the system can timely send an early warning signal to remind the driver to take corresponding measures to avoid further aggravation of the fault.

[0093] Figure 5 It is a structural schematic diagram of an electronic device according to some embodiments of the present application. As shown in Figure 5 The electronic device 500 includes a processor 501 having one or more processing cores, a memory 502 having one or more computer readable storage media, and a computer program stored on the memory 502 and executable on the processor. The processor 501 is electrically connected to the memory 502.

[0094] The processor 501 is the control center of the electronic device 500, and connects all parts of the electronic device 500 through various interfaces and lines. By running or loading the software program and / or unit stored in the memory 502 and calling the data stored in the memory 502, the processor 501 can execute various functions and process data of the electronic device 500, thereby monitoring the whole electronic device 500. The processor 501 can be a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a network processor (NP), etc., and can realize or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present application.

[0095] In the embodiments of the present application, the processor 501 in the electronic device 500 loads the computer program corresponding to the process of one or more application programs into the memory 502 according to the method or steps of the above embodiments, and runs the application program stored in the memory 502 by the processor 501, so as to execute the motor fault detection method.

[0096] According to the electronic device of the embodiments of the present application, the motor fault level can be determined directly based on the motor fault characteristic parameter by executing the above motor fault detection method, without occupying too much processor resource and with low required hardware cost, thereby solving the problems of the prior art that the fault cannot be diagnosed quickly and accurately and the cost is high.

[0097] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the motor fault detection method described above. For example, the computer readable storage medium can be the memory described above including program instructions, and the program instructions can be executed by the processor of the electronic device to realize or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present application.

[0098] The embodiments of the present application also provide a computer program product, which stores instructions, and the instructions are executed by a computer to implement the motor fault detection method described above. For example, the instructions are executed by the computer to realize or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present application.

[0099] The embodiments of the present application also provide a vehicle, which includes the electronic device described above, or the detection system described above, or the motor and the controller, and the controller is used to execute the motor fault detection method described above. The vehicle can be a fuel automobile, a plug-in hybrid electric vehicle or a new energy vehicle, and the present specification does not make specific limitation on this.

[0100] According to the vehicle of the embodiments of the present application, the motor fault level can be determined directly based on the motor fault characteristic parameter by executing the motor fault detection method by the electronic device or the control system or the controller, without occupying too much processor resource and with low required hardware cost, thereby solving the problems of the prior art that the fault cannot be diagnosed quickly and accurately and the cost is high.

[0101] The above embodiments are only used to illustrate the technical solutions of the control method of the opening and closing member applied to the vehicle, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the control method can also be applied to the field of smart home, and it does not make the essence of the corresponding technical solution deviate from the scope of the technical solutions of the embodiments of the present application.

[0102] In one embodiment, the vehicle can be configured in a fully or partially autonomous driving mode. For example, the vehicle can control itself while in the autonomous driving mode and can determine a current state of the vehicle and its surrounding environment, determine a possible behavior of at least one other vehicle in the surrounding environment, and determine a confidence level corresponding to a likelihood that the other vehicle will perform the possible behavior, based on the determined information, control the vehicle. While the vehicle is in the autonomous driving mode, the vehicle can be placed to operate without human interaction.

[0103] In the description of the present application, the terms "first", "second", etc. are used only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0104] The embodiments, implementation manners and related technical features of the present application can be combined, replaced with each other without conflict.

[0105] The above is only the preferred embodiment of the present application, and does not limit the present application in any form. Although the description of each embodiment in the embodiment of the present application has its own emphasis, the part not described in detail in a certain embodiment can be referred to the related embodiment of other embodiments. Any simple modification, equivalent change and modification made according to the technical essence of the present application to the above embodiments without departing from the technical solution content of the present application still belongs to the scope of the technical solution of the present application.

Claims

1. A motor fault detection method, characterized in that: include: The motor fault level is determined based on motor fault characteristic parameters, where the motor fault characteristic parameters are determined based on accelerations in the X-axis direction, the Y-axis direction, and the Z-axis direction of the motor.

2. The method according to claim 1, characterized in that Determining the motor fault level based on the motor fault characteristic parameters includes: The motor fault level is determined based on the motor fault characteristic parameters and the set threshold.

3. The method according to claim 2, characterized in that The step of determining the motor fault level based on the motor fault characteristic parameters and a set threshold value includes: When the motor fault characteristic parameter is greater than or equal to the first threshold and less than the second threshold, the motor fault level is determined to be a first level fault; and / or, When the motor fault characteristic parameter is greater than or equal to the second threshold and less than the third threshold, the motor fault level is determined to be a second level fault.

4. The method according to claim 3, characterized in that The step of determining the motor fault level based on the motor fault characteristic parameters and a set threshold value includes: When the motor fault characteristic parameter is greater than or equal to the third threshold, the motor fault level is determined to be a third level fault.

5. The method according to claim 1, wherein Determining the motor fault level based on the motor fault characteristic parameters includes: Find the motor fault level corresponding to the motor fault characteristic parameters from the correspondence table.

6. The method according to claim 1, characterized in that The method further comprises: Calculate normalized data values ​​based on the sampling values ​​of the motor in the X-axis direction, the Y-axis direction, and the Z-axis direction; The motor fault characteristic parameter is determined according to the normalized data value.

7. The method according to claim 6, characterized in that The method further comprises: Get the acceleration of the motor in the X-axis, Y-axis and Z-axis directions; The sampling values ​​of the motor in the X-axis direction, the Y-axis direction, and the Z-axis direction are determined according to the acceleration.

8. The method according to any one of claims 1 to 6, characterized in that After determining the motor fault level based on the motor fault characteristic parameters, the method further includes: When the motor is in the first fault level or the second fault level, a prompt message is output.

9. The method according to any one of claims 1 to 6, characterized in that After determining the motor fault level based on the motor fault characteristic parameters, the method further includes: When the motor is in the third fault level, the motor is controlled to stop running.

10. A motor fault detection system, characterized in that: include: The controller is used to determine the motor fault level based on the motor fault characteristic parameters, wherein the motor fault characteristic parameters are determined based on the acceleration of the motor in the X-axis direction, the Y-axis direction and the Z-axis direction.

11. The system according to claim 10, wherein: The system further comprises: The acceleration sensor is connected to the controller and is used to detect the acceleration of the motor in the X-axis direction, the Y-axis direction and the Z-axis direction.

12. The system according to claim 11, wherein: The system further comprises: an amplification circuit, connected to the acceleration sensor and the controller, for outputting sampling values ​​of the motor in the X-axis direction, the Y-axis direction, and the Z-axis direction based on the acceleration of the motor in the X-axis direction, the Y-axis direction, and the Z-axis direction; The controller is further configured to determine motor fault characteristic parameters based on sampling values ​​of the motor in the X-axis direction, the Y-axis direction, and the Z-axis direction.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the computer implements the motor fault detection method according to any one of claims 1 to 9 .

14. A computer program product, characterized in that The computer program product stores instructions, which, when executed by a computer, cause the computer to implement the motor fault detection method according to any one of claims 1 to 9 .

15. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the motor fault detection method according to any one of claims 1 to 9.

16. A vehicle, characterized in that: include: The motor fault detection system according to any one of claims 10 to 12; Or, the electronic device according to claim 15; Alternatively, a motor and a controller, wherein the controller is configured to execute the motor fault detection method according to any one of claims 1 to 9.

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