Method, device and equipment for evaluating fault risk of permanent magnet synchronous motor, and medium

Abstract

The invention discloses a method, a device and equipment for evaluating the fault risk of a permanent magnet synchronous motor, and a medium. The method comprises the following steps: initializing the working state of the permanent magnet synchronous motor; the magnetic field intensity of the permanent magnet synchronous motor in the working process is monitored in real time, and the permanent magnet residual magnetism variable # imgabs 1 # and the environment temperature # imgabs 2 # at the fault # imgabs 0 # moment are obtained; inputting a permanent magnet residual magnetism variable # imgabs3 # and an environment temperature # imgabs4 # into the risk assessment model to generate a demagnetization failure rate and a performance change rate at a # imgabs5 # moment; and a demagnetization risk value is obtained based on the product of the demagnetization failure rate and the performance change rate, if the demagnetization risk value exceeds a preset failure threshold value, it is judged that the motor is in a failure state, evaluation is ended, and if not, failure risk evaluation at the next moment is carried out. The fault evolution process is dynamically tracked by introducing the dynamic Bayesian network DBN time slice and the real-time data closed loop.

Description

Technical Field

[0001] The present invention relates to the technical field of synchronous motors, and in particular to a method, device, equipment and medium for evaluating the failure risk of a permanent magnet synchronous motor. Background Art

[0002] Failure Mode and Effects Analysis (FMEA) is an inductive analysis method that analyzes all possible failure modes for each product in a system and their potential impacts on the system, categorizing each failure mode by frequency, severity, and detection difficulty. This method, based on expert evaluation results and combining the three risk factors listed above, ranks the criticality of failure modes. It is a qualitative / semi-quantitative risk assessment method commonly used during the product design phase.

[0003] Traditional FMEA primarily assesses risk by calculating and comparing the Risk Priority Number (RPN) of failure modes. The RPN is calculated by multiplying three risk factors ranging from 1 to 10: Occurrence (O), Severity (S), and Detectability (D). However, this method only analyzes failure relationships at fixed points in time and does not model the temporal characteristics of failure evolution. It is unable to capture the dynamic evolution of failure risks (such as component degradation and environmental changes over time), ultimately leading to delayed warnings for progressive failures (such as permanent magnet demagnetization) and sudden failures (short circuits). Furthermore, the scoring process is highly subjective, and the risk priority calculation ignores linear relationships. Furthermore, the system lacks real-time and predictive capabilities, resulting in a disconnect between risk assessment results and actual risks in variable operating conditions (such as changes in the robot's critical load). Summary of the Invention

[0004] In order to solve the above technical problems, the present invention proposes a method, device, equipment and medium for evaluating the fault risk of a permanent magnet synchronous motor. By constructing a dynamic Bayesian network and introducing time slicing to describe the fault propagation path to achieve a data closed loop, and systematically constructing a fault mode and effect analysis framework, a quantitative assessment of the fault risk is achieved.

[0005] In order to achieve the above object, the present invention provides the following solutions: In a first aspect, the present invention provides a method for assessing the failure risk of a permanent magnet synchronous motor, the method comprising: Initializing the working state of the permanent magnet synchronous motor; Real-time monitoring of the magnetic field strength during the operation of the permanent magnet synchronous motor. At this moment, the permanent magnet synchronous motor fails, and the The remanent magnetization variable of the permanent magnet at the moment and ambient temperature ; The permanent magnet remanence variable and ambient temperature Input risk assessment model generation Demagnetization failure rate at time and performance change rate ; Based on the demagnetization failure rate and performance change rate The product of the demagnetization risk value is obtained , if the demagnetization risk value If the fault threshold is exceeded, the motor is judged to be in a fault state and the evaluation ends. If not, the fault risk assessment for the next moment is performed.

[0006] Further, obtaining the The remanent magnetization variable of the permanent magnet at the moment The specific steps are to use the back electromotive force method to obtain Magnetic field strength at the moment , which is expressed as follows: ; Where E is the back electromotive force, is the rotation speed, is the motor constant.

[0007] Furthermore, the risk assessment model includes a permanent magnet remanence degradation sub-model and a performance risk assessment sub-model based on a Bayesian network.

[0008] Furthermore, the permanent magnet remanence degradation sub-model based on the Bayesian network is specifically: A non-homogeneous state transition model based on Bayesian network is constructed. In the non-homogeneous state transition model, the state transition probability changes dynamically with time and temperature and is expressed as: ; The remanence is updated based on the performance degradation curve of the non-homogeneous state transition model and is expressed as: ; pass calculate Demagnetization failure rate at the time of failure ; in, represents the degradation rate, It is at temperature The degradation rate under , Q, K are constants, is the transition probability function, Represents the dynamic characteristics of the system.

[0009] Furthermore, the performance risk assessment sub-model specifically obtains the performance change rate according to the torque change and current change before and after demagnetization. , and expressed as: ; Indicates the torque change value before and after demagnetization, Indicates the current change value before and after demagnetization.

[0010] Furthermore, the torque change before and after demagnetization Expressed as: ; Current change before and after demagnetization Expressed as: ; in, represents the initial working torque, Indicates The working torque when demagnetization fault occurs at any moment, represents the initial current, Indicates The operating current when demagnetization fault occurs at any time.

[0011] Furthermore, the method further comprises: The demagnetization risk value at the moment is used to calculate the comprehensive risk value for the entire time period , expressed as: ; The entire time period is a time range based on a dynamic Bayesian network.

[0012] In a second aspect, the present invention provides a fault risk assessment method for a permanent magnet synchronous motor, which is used to implement the fault risk assessment method for a permanent magnet synchronous motor in the first aspect. The device includes: An initialization module, used to initialize the working state of the permanent magnet synchronous motor; The monitoring module is used to monitor the magnetic field strength of the permanent magnet synchronous motor in real time during operation. At this moment, the permanent magnet synchronous motor fails, and the The remanent magnetization variable of the permanent magnet at the moment and ambient temperature ; A generating module for converting the permanent magnet remanence variable and ambient temperature Input risk assessment model generation Demagnetization failure rate at time and performance change rate ; Evaluation module for the demagnetization failure rate based on the and performance change rate The product of the demagnetization risk value is obtained , if the demagnetization risk value If the fault threshold is exceeded, the motor is judged to be in a fault state and the evaluation ends. If not, the fault risk assessment for the next moment is performed.

[0013] In a third aspect, the present invention further proposes an electronic device comprising a memory and a processor, wherein the memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the method for assessing the failure risk of a permanent magnet synchronous motor as described in the first aspect.

[0014] In a fourth aspect, the present invention further proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for assessing the failure risk of a permanent magnet synchronous motor as described in the first aspect.

[0015] This invention dynamically tracks fault evolution by integrating time slicing with a real-time data closed loop using a dynamic Bayesian network (DBN). Monitoring data (temperature) is fed into the DBN in real time, updating the conditional probability table (CPT) and correcting fault probabilities. This reduces subjective bias and improves the reliability of the RPN. Driven by real-time data, this approach incorporates an online data closed loop with parameter updates, dynamically adjusting CPT parameters based on a state transition method to adapt to conditions such as load fluctuations and ambient temperature changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of the method for assessing the failure risk of a permanent magnet synchronous motor proposed in the present invention.

[0017] Figure 2 This is a Bayesian network structure diagram for the failure risk assessment method of a permanent magnet synchronous motor proposed in the present invention.

[0018] Figure 3 This is a dynamic Bayesian network structure diagram for the entire time period in the method for evaluating the failure risk of a permanent magnet synchronous motor proposed in the present invention.

[0019] Figure 4 This is the PMSM finite element simulation diagram in the permanent magnet synchronous motor failure risk assessment method proposed in the present invention.

[0020] Figure 5 This is the torque diagram at the initial moment in the method for evaluating the failure risk of a permanent magnet synchronous motor proposed in the present invention.

[0021] Figure 6This is a finite element simulation diagram of the failure risk assessment method of the permanent magnet synchronous motor proposed in the present invention.

[0022] Figure 7 This is the torque diagram after demagnetization in the failure risk assessment method of the permanent magnet synchronous motor proposed in the present invention. DETAILED DESCRIPTION

[0023] The present application is described in further detail below in conjunction with the accompanying drawings. It is necessary to point out that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technicians in this field can make some non-essential improvements and adjustments to the present application based on the above application content.

[0024] like Figure 1 As shown, the present invention proposes a method for evaluating the failure risk of a permanent magnet synchronous motor, the method comprising: S101 initializes the working state of the permanent magnet synchronous motor; S102. Real-time monitoring of the magnetic field strength of the permanent magnet synchronous motor during operation. At this moment, the permanent magnet synchronous motor fails, and the The remanent magnetization variable of the permanent magnet at the moment and ambient temperature ; S103. The permanent magnet remanence variable and ambient temperature Input risk assessment model generation Demagnetization failure rate at time and performance change rate ; S104. Based on the demagnetization failure rate and performance change rate The product of the demagnetization risk value is obtained , if the demagnetization risk value If the fault threshold is exceeded, the motor is judged to be in a fault state and the evaluation ends. If not, the fault risk assessment for the next moment is performed.

[0025] In this embodiment, the working state of the permanent magnet synchronous motor is initialized and the The state node at the moment is is in normal state. At the same time, the initial permanent magnet remanence variable .

[0026] After the initialization of the permanent magnet synchronous motor is completed, the synchronous motor enters the working state. Real-time monitoring of the magnetic field strength of the permanent magnet synchronous motor during operation. At this moment, the permanent magnet synchronous motor fails, that is, For fault status, obtain the The remanent magnetization variable of the permanent magnet at the moment and ambient temperature In this process, the The remanent magnetization variable of the permanent magnet at the moment The specific steps are to use the back electromotive force method to obtain Magnetic field strength at the moment , which is expressed as follows: ; Where E is the back electromotive force, is the rotation speed, is the motor constant.

[0027] By driving the motor to a constant speed under no-load conditions and measuring the back electromotive force at both ends of the winding, the magnetic field strength can be calculated according to the above formula .

[0028] In this embodiment, the risk value is evaluated by a risk assessment model, such as Figure 2 As shown in the figure, a permanent magnet remanence degradation sub-model based on Bayesian network is adopted, which includes the working environment node , status node , degenerate nodes and consequence nodes .

[0029] In the non-homogeneous state transition model, the state transition probability changes dynamically with time and temperature and is expressed as: ; The remanence is updated based on the performance degradation curve of the non-homogeneous state transition model and is expressed as: ; pass calculate Demagnetization failure rate at the time of failure ; in, represents the degradation rate, It is at temperature The degradation rate under , Q, K are constants, is the transition probability function, Represents the dynamic characteristics of the system.

[0030] In this embodiment, the performance risk assessment sub-model specifically obtains the performance change rate according to the torque change and current change before and after demagnetization. , and expressed as: ; Indicates the torque change value before and after demagnetization, Indicates the current change value before and after demagnetization.

[0031] ; ; in, represents the initial working torque, Indicates The working torque when demagnetization fault occurs at any moment, represents the initial current, Indicates The operating current when demagnetization fault occurs at any time.

[0032] In this embodiment, the calculation formula of torque is: ; in, is the vacuum permeability, is the effective axial length of the motor, R is the radius for calculating torque, usually the air gap diameter, is the radial magnetic induction intensity component, is the tangential magnetic induction intensity component. According to the above formula, when t is 0, it is the initial working torque. If t is not 0, the torque changes, which is .

[0033] Through the above content, based on the demagnetization failure rate and performance change rate The product of the demagnetization risk value is obtained , if the demagnetization risk value If the fault threshold is exceeded, the motor is judged to be in a fault state and the evaluation ends. If not, the fault risk assessment for the next moment is performed.

[0034] ; In this embodiment, a non-homogeneous state transition model is used to calculate the state of the next time step: exist Moment prediction The risk value at the moment, so we need to predict The status, failure rate and failure consequences at the moment are used to predict the risk value. The current state is known at all times and calculated according to the degradation curve Moment status.

[0035] Update remanence according to performance degradation curve: ; In this embodiment, the expression for the degradation process of the permanent magnet magnetic field strength based on the acceleration factor is: ; in, represents the magnetic field strength under the influence of temperature and time, in, is the initial magnetic field strength at temperature T, is the decay rate at temperature T, indicating that the magnetic field intensity decays exponentially with time, which follows the Arrhenius equation, namely ; Where A refers to the frequency factor, is the activation energy, R is the gas constant, and T is the absolute temperature; the acceleration factor AF for the accelerated experiment is: ; Where: AF is the acceleration factor, T1 is the normal use temperature, and T2 is the accelerated test temperature. According to the above degradation curve, the temperature T and time t are substituted into the magnetic field intensity to be deduced theoretically.

[0036] In this embodiment, if Figure 3 As shown in the figure, it is a dynamic Bayesian network development diagram, which can obtain the comprehensive risk in the entire time period, expressed as: ; In this embodiment, if Figure 4 The figure shows the finite element simulation diagram of PMSM. The permanent magnet used in the motor is NdF35. Through the acceleration experiment, it is found that the surface magnetic field intensity changes with time and satisfies the exponential degradation law, that is: ; in is the degradation rate. Assume that the remanence threshold is , if the remanence at a certain moment is , then the demagnetization failure rate is: ; like Figure 5-6 As shown in the figure, at the initial moment, the surface magnetic field strength of the permanent magnet is about 1547.18 Gauss, and the motor torque is about 796mN. Remanence at the moment The demagnetization failure rate is obtained as: ; according to Figure 7 The simulation results shown are as follows: ; At this time, the current value and average value have hardly changed, so is 0, then: ; Figure 7 As shown, the average torque at this moment is about 672.48 mN, and the torque decrease rate is about 0.1552.

[0037] This embodiment further proposes a permanent magnet synchronous motor fault risk assessment device, which is used to implement the permanent magnet synchronous motor fault risk assessment method in the above embodiment. The device includes: An initialization module, used to initialize the working state of the permanent magnet synchronous motor; The monitoring module is used to monitor the magnetic field strength of the permanent magnet synchronous motor in real time during operation. At this moment, the permanent magnet synchronous motor fails, and the The remanent magnetization variable of the permanent magnet at the moment and ambient temperature ; A generating module for converting the permanent magnet remanence variable and ambient temperature Input risk assessment model generation Demagnetization failure rate at time and performance change rate ; Evaluation module for the demagnetization failure rate based on the and performance change rate The product of the demagnetization risk value is obtained , if the demagnetization risk value If the fault threshold is exceeded, the motor is judged to be in a fault state and the evaluation ends. If not, the fault risk assessment for the next moment is performed.

[0038] This embodiment further provides an electronic device. The electronic device may include: a processor, a communication interface, a memory, and a communication bus. The processor, the communication interface, and the memory communicate with each other via the communication bus. The processor may invoke logic instructions in the memory to execute the above-described method for assessing the failure risk of a permanent magnet synchronous motor.

[0039] In addition, the logical instructions in the above-mentioned memory can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0040] This embodiment further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for evaluating the failure risk of a permanent magnet synchronous motor provided by the above methods is implemented.

[0041] In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood broadly. For example, they may refer to fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0042] In the present invention, unless otherwise expressly specified or limited, when a first feature is "above" or "below" a second feature, it may mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediary. Furthermore, when a first feature is "above," "above," or "above" a second feature, it may mean that the first feature is directly above or diagonally above the second feature, or simply means that the first feature is at a higher level than the second feature. When a first feature is "below," "below," or "below" a second feature, it may mean that the first feature is directly below or diagonally below the second feature, or simply means that the first feature is at a lower level than the second feature.

[0043] The above-described embodiments merely illustrate several implementations of the present invention. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, and all such variations and improvements fall within the scope of protection of the present invention.

Claims

1. A method for assessing the failure risk of a permanent magnet synchronous motor, characterized in that: The method comprises the following steps: Initializing the working state of the permanent magnet synchronous motor; Real-time monitoring of the magnetic field strength during the operation of the permanent magnet synchronous motor. k At this moment, the permanent magnet synchronous motor fails, and the t k The remanent magnetization variable of the permanent magnet at the moment and ambient temperature ; The permanent magnet remanence variable and ambient temperature Input risk assessment model generation Demagnetization failure rate at time and performance change rate ; Based on the demagnetization failure rate and performance change rate The product of the demagnetization risk value is obtained , if the demagnetization risk value If the fault threshold is exceeded, the motor is judged to be in a fault state and the evaluation ends. If not, the fault risk assessment for the next moment is performed.

2. The method for evaluating the failure risk of a permanent magnet synchronous motor according to claim 1, wherein: Get the t k The remanent magnetization variable of the permanent magnet at the moment The specific steps are to use the back electromotive force method to obtain t k Magnetic field strength at the moment , which is expressed as follows: ； Where E is the back electromotive force, is the rotation speed, is the motor constant.

3. The method for evaluating the failure risk of a permanent magnet synchronous motor according to claim 2, wherein: The risk assessment model includes a permanent magnet remanence degradation sub-model based on a Bayesian network and a performance risk assessment sub-model.

4. The method for evaluating the failure risk of a permanent magnet synchronous motor according to claim 3, wherein: The permanent magnet remanence degradation sub-model based on the Bayesian network is specifically: A non-homogeneous state transition model based on Bayesian network is constructed. In the non-homogeneous state transition model, the state transition probability changes dynamically with time and temperature and is expressed as: ; The remanence is updated based on the performance degradation curve of the non-homogeneous state transition model and is expressed as: ; pass calculate Demagnetization failure rate at the time of failure ; in, represents the degradation rate, It is at temperature The degradation rate under the condition of , Q and K are constants, f is the transition probability function, and θ represents the dynamic characteristics of the system.

5. The method for evaluating the failure risk of a permanent magnet synchronous motor according to claim 3, wherein: The performance risk assessment sub-model specifically obtains the performance change rate based on the torque change and current change before and after demagnetization. , and expressed as: ； Indicates the torque change value before and after demagnetization, Indicates the current change value before and after demagnetization.

6. The method for evaluating the failure risk of a permanent magnet synchronous motor according to claim 5, wherein: Torque change before and after demagnetization Expressed as: ； Current change before and after demagnetization Expressed as: ； in, represents the initial working torque, Indicates The working torque when demagnetization fault occurs at any time, represents the initial current, Indicates The operating current when demagnetization fault occurs at any time.

7. The method for evaluating the failure risk of a permanent magnet synchronous motor according to claim 5, wherein: The method further comprises: k The demagnetization risk value at the moment is used to calculate the comprehensive risk value for the entire time period , expressed as: ； The entire time period is a time range based on a dynamic Bayesian network.

8. An evaluation device for the method for evaluating the failure risk of a permanent magnet synchronous motor according to any one of claims 1 to 7, characterized in that: The device comprises: An initialization module, used to initialize the working state of the permanent magnet synchronous motor; The monitoring module is used to monitor the magnetic field strength of the permanent magnet synchronous motor in real time during operation. k At this moment, the permanent magnet synchronous motor fails, and the t k The remanent magnetization variable of the permanent magnet at the moment and ambient temperature ; A generating module for converting the permanent magnet remanence variable and ambient temperature Input risk assessment model generation Demagnetization failure rate at time and performance change rate ; Evaluation module for the demagnetization failure rate based on the and performance change rate The product of the demagnetization risk value is obtained , if the demagnetization risk value If the fault threshold is exceeded, the motor is judged to be in a fault state and the evaluation ends. If not, the fault risk assessment for the next moment is performed.

9. An electronic device, characterized in that: It includes a memory and a processor, and is characterized in that the memory is coupled to the processor; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the method for evaluating the failure risk of a permanent magnet synchronous motor as described in any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for evaluating the failure risk of a permanent magnet synchronous motor according to any one of claims 1 to 5 is implemented.

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