Motor self-learning fault judgment method, system and device and readable storage medium
By using multi-dimensional parameters to determine motor self-learning faults, the problem of single-dimensional judgment in existing technologies is solved, achieving more accurate fault identification and stability of the self-learning process.
Patent Information
- Application Number
- CN202511329462.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-17
- Publication Date
- 2026-01-06
AI Technical Summary
Existing motor self-learning technologies are too simplistic in determining success, failing to effectively identify or eliminate other faults that could lead to self-learning failure, especially in high-speed or heavy-load scenarios.
By introducing multi-dimensional parameters such as real-time motor speed, target speed, real-time quadrature-axis current, real-time direct-axis current, and the running time of the self-learning phase, combined with preset duration and threshold, it is possible to determine whether there are faults such as timeout, overspeed, stall, or current over-limit during the motor self-learning process.
This enables more accurate and comprehensive judgment of motor self-learning faults, ensuring the smooth completion of the self-learning process and improving the stability and reliability of the system.
Smart Images
Figure CN121283318A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of motor technology, specifically to a method, system, device, and readable storage medium for judging motor self-learning faults. Background Technology
[0002] With the widespread application of permanent magnet synchronous motors in high-efficiency vector control, accurate rotor position information has become a key factor in achieving efficient operation. If there is a deviation in the initial position of the motor, the actual output torque will deviate from the theoretical value, leading to problems such as starting failure, torque fluctuations, and current distortion. In severe cases, it can even damage the motor and controller, especially in high-speed or heavy-load scenarios where these problems are more pronounced.
[0003] Traditional mechanical sensors (such as encoders) suffer from high costs and susceptibility to interference, and their accuracy may be affected by mechanical wear or installation errors during long-term use. Therefore, sensorless technology and initial position self-learning technology have become important directions for improving the reliability of motor systems and reducing costs.
[0004] However, existing motor self-learning technology still faces some technical challenges in its implementation. For example, the judgment of whether self-learning is successful usually depends on whether the deviation between the motor speed and the preset speed is within a predetermined threshold range and a simple judgment is made within a set time. This judgment method is too simplistic and cannot effectively identify or eliminate other faults that may cause self-learning failure.
[0005] Therefore, how to accurately diagnose motor self-learning faults is an urgent problem to be solved. Summary of the Invention
[0006] This application provides a method, system, device, and readable storage medium for judging motor self-learning faults, which can accurately judge motor self-learning faults.
[0007] In a first aspect, embodiments of this application provide a method for determining a motor self-learning fault, the method comprising: The judgment result of the motor self-learning fault is determined based on one or more of the target parameters. The judgment result includes timeout fault, overspeed fault, stall fault and current over-limit fault. The target parameters include real-time motor speed, target speed, real-time quadrature axis current, real-time direct axis current and running time of the self-learning stage.
[0008] In conjunction with the first aspect, in one implementation, determining the judgment result of the motor self-learning fault based on one or more parameters in the target parameters includes: The system determines whether a self-learning timeout fault exists based on the runtime of the self-learning phase and a preset first duration threshold. The system determines whether a self-learning overspeed fault exists based on the real-time motor speed, the target speed, and the preset second duration threshold. The presence of a self-learning stall fault is determined based on real-time motor speed, target speed, preset speed threshold, preset speed difference threshold, and preset third duration threshold. The system determines whether a self-learning current over-limit fault exists based on the real-time direct-axis current, real-time quadrature-axis current, preset current threshold, and preset fourth duration threshold.
[0009] In conjunction with the first aspect, in one implementation, the target parameters include the runtime of the self-learning phase, the self-learning phase including a phase for acquiring the initial motor angle, a phase for acquiring the reference current, and a phase for acquiring the angle compensation value, and determining the judgment result of the motor self-learning fault based on one or more parameters in the target parameters includes: Determine whether the runtime of each stage in the self-learning phase is not greater than the preset first duration threshold. If so, the result of the self-learning fault judgment is that there is no self-learning timeout fault; If not, the result of the self-learning fault judgment is that there is a self-learning timeout fault.
[0010] In conjunction with the first aspect, in one implementation, the target parameters include real-time motor speed and target speed, and determining the judgment result of motor self-learning fault based on one or more of the target parameters includes: Determine whether the real-time motor speed is greater than the target speed and whether the duration for which the real-time motor speed is greater than the target speed is greater than a preset second duration threshold. If so, the result of the self-learning fault judgment is that there is a self-learning overspeed fault; If not, the result of the self-learning fault determination is that there is no self-learning overspeed fault.
[0011] In conjunction with the first aspect, in one implementation, the target parameters include real-time motor speed and target speed, and the determination of the judgment result of motor self-learning fault based on one or more of the target parameters further includes: The speed difference is determined based on the real-time motor speed and the target speed; Determine whether the speed difference is greater than a preset difference threshold, whether the duration for which the speed difference is greater than the preset difference threshold is greater than a preset third duration threshold, and whether the real-time motor speed is not greater than a preset speed threshold. If so, the result of the self-learning fault judgment is that there is a self-learning stall fault; If not, the result of the self-learning fault determination is that there is no self-learning stall fault.
[0012] In conjunction with the first aspect, in one implementation, the target parameters include real-time direct-axis current and real-time quadrature-axis current, and determining the judgment result of the motor self-learning fault based on one or more of the target parameters includes: The target current is determined based on the real-time direct-axis current and the real-time quadrature-axis current. Determine whether the target current is greater than a preset current threshold and whether the duration for which the target current is greater than the preset current threshold is greater than a preset fourth duration threshold. If so, the result of the self-learning fault judgment is that there is a self-learning current over-limit fault; If not, the result of the self-learning fault judgment is that there is no self-learning current over-limit fault.
[0013] Secondly, embodiments of this application provide a system for determining motor self-learning faults, the system comprising: The judgment module is used to determine the judgment result of the motor self-learning fault based on one or more of the target parameters. The judgment result includes timeout fault, overspeed fault, stall fault and current over-limit fault. The target parameters include real-time motor speed, target speed, real-time quadrature-axis current, real-time direct-axis current and running time of the self-learning stage.
[0014] In conjunction with the second aspect, in one implementation, the determination module is specifically used for: The system determines whether a self-learning timeout fault exists based on the runtime of the self-learning phase and a preset first duration threshold. The system determines whether a self-learning overspeed fault exists based on the real-time motor speed, the target speed, and the preset second duration threshold. The presence of a self-learning stall fault is determined based on real-time motor speed, target speed, preset speed threshold, preset speed difference threshold, and preset third duration threshold. The system determines whether a self-learning current over-limit fault exists based on the real-time direct-axis current, real-time quadrature-axis current, preset current threshold, and preset fourth duration threshold.
[0015] Thirdly, embodiments of this application provide a motor self-learning fault determination device, the motor self-learning fault determination device including a processor, a memory, and a motor self-learning fault determination program stored in the memory and executable by the processor, wherein when the motor self-learning fault determination program is executed by the processor, it implements the steps of the motor self-learning fault determination method as described in any of the preceding claims.
[0016] Fourthly, embodiments of this application provide a computer-readable storage medium storing a motor self-learning fault judgment program, wherein when the motor self-learning fault judgment program is executed by a processor, it implements the steps of the motor self-learning fault judgment method as described in any of the preceding claims.
[0017] The beneficial effects of the technical solutions provided in this application include: The judgment results of motor self-learning faults, including timeout faults, overspeed faults, stall faults, and current over-limit faults, are determined by one or more of the target parameters, including real-time motor speed, target speed, real-time quadrature-axis current, real-time direct-axis current, and running time of the self-learning phase. This application introduces more dimensions of parameters (speed, current, duration, etc.) to judge motor self-learning faults. Compared with the traditional single speed deviation judgment method, it can more accurately and comprehensively identify the fault problems that may occur during the motor self-learning process, thereby achieving more accurate fault judgment. Attached Figure Description
[0018] Figure 1 This is a flowchart illustrating the motor self-learning process in an embodiment of the motor self-learning fault judgment method of this application. Figure 2 This is a flowchart illustrating an embodiment of the motor self-learning fault judgment method of this application; Figure 3 This is a schematic diagram of the hardware structure of the motor self-learning fault judgment device involved in the embodiment of this application. Detailed Implementation
[0019] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.
[0020] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.
[0021] In a first aspect, embodiments of this application provide a method for determining motor self-learning faults.
[0022] In one embodiment, the method for determining motor self-learning faults includes: Step S10: Determine the judgment result of the motor self-learning fault based on one or more of the target parameters. The judgment result includes timeout fault, overspeed fault, stall fault and current over-limit fault. The target parameters include real-time motor speed, target speed, real-time quadrature-axis current, real-time direct-axis current and running time of the self-learning stage.
[0023] In this embodiment, as an example, the target speed is a preset ideal speed used to determine whether the motor has reached a predetermined operating standard. Target parameters include real-time motor speed, target speed, real-time quadrature-axis current, real-time direct-axis current, and the duration of the self-learning phase. Real-time motor speed refers to the current speed of the motor; comparing it with the target speed helps determine whether the motor is operating normally. Real-time quadrature-axis current represents the current value of the motor in the quadrature-axis direction, and real-time direct-axis current represents the current value of the motor in the direct-axis direction. These two parameters help determine whether the motor is under normal load. The duration of the self-learning phase refers to the duration of each stage in the motor's self-learning phase from start to finish. The judgment results include timeout faults, overspeed faults, stall faults, and current over-limit faults. By comprehensively considering one or more of the above target parameters, the system can accurately determine whether the motor has experienced timeout, overspeed, stall, or current over-limit faults, thereby ensuring the smooth completion of the self-learning process.
[0024] It should be noted that before the motor enters self-learning mode, it first checks whether a self-learning request exists and whether the motor status is normal. When the motor controller receives a motor zero-position self-learning request from the vehicle controller and the motor status is normal and fault-free, it enters the motor self-learning function. Motor self-learning includes three stages: acquiring the initial motor angle, acquiring the reference current, and acquiring the angle compensation value. Figure 1 As shown below, the motor self-learning process is described: Obtain the initial motor angle (i.e., coarsely adjust the initial motor angle): by sending a fixed value I to the d-axis current. d_ref (This fixed value can preferably be 1 / 4 of the motor's rated current). With the q-axis current set to 0, the system output vector is (1, 0, 0), representing the direction and state of motor control. "1" indicates that the upper arm of the U-phase is conducting, and "0" indicates that the lower arm of the VW-phase is conducting. That is, current is input to the motor from the U-phase and output from the VW-phase. In this way, the motor's rotor position is set to the 0 position. When the d-axis current I... d_fed With a given current I d_refWhen the difference between the values is less than 20A and the rotor position of the motor no longer changes, the angle measured by the resolver (i.e., the angle sensor) is the initial angle of the motor. It should be noted that in the process of obtaining the initial angle of the motor, after determining that there is no self-learning fault through one or more of the above target parameters, the coarse adjustment completion marker position 1 can be obtained.
[0025] Obtain the q-axis reference current The motor is given a target speed through a preset speed control mode, causing it to idle at low speed for a period of time. During this period, the average value of the q-axis current is recorded and used as a reference current for later operation. It should be noted that during the process of obtaining the reference current, the flag position 1 indicating that the acquisition of the reference current is complete can be determined by one or more of the above target parameters after determining that there is no self-learning fault.
[0026] Obtain angle compensation value Injecting a large percentage of field-weakening current into the motor (Preferably half of the rated current), at this time, read the q-axis feedback current of the motor. If the initial angle of the motor is off at this point, and There will inevitably be a deviation I between them. err To correct this deviation, a PI controller can be used, where the input of the PI controller is I. err (i.e., q-axis current deviation) and The difference), the output is the angle compensation value. This is done to adjust the rotor position of the motor, thereby correcting it to the correct position. Meanwhile, to prevent over-correction, an angle compensation value is used. The angle is limited to ±10° to avoid excessive corrections that could cause other problems; the system forms a closed-loop control by continuously adjusting angle compensation, when I... err If the error is less than one error band and lasts for 4 seconds, it means that accurate identification is complete. It should be noted that during the process of obtaining the angle compensation value, one or more of the above target parameters can be used to determine that there is no self-learning fault in this process. After that, the flag position 1 indicating that the reference angle compensation value has been obtained is set, and self-learning is exited.
[0027] Understandably, if the judgment result indicates that there is a self-learning fault, it is possible to determine whether the number of self-learning failures exceeds a preset threshold (e.g., 3 times) within a certain period of time. If the number of failures exceeds the preset threshold, it means that self-learning has failed. If the number of failures does not exceed the preset threshold, the step of determining whether there is a self-learning request and whether the motor status is normal is re-executed.
[0028] Understandably, the formula for calculating current deviation is:
[0029] In the formula, This is the q-axis reference current; This is the q-axis feedback current; This is the current deviation.
[0030] The formula for calculating the angle compensation value is:
[0031] In the formula, For proportional gain; This is the integral gain; This is the step size for the self-learning program. This refers to the angle compensation value. It should be noted that the specific values of the proportional gain and integral gain can be obtained through actual debugging and calibration, and are not limited here. The specific value of the running step size of the self-learning program can be determined according to actual needs, and is not limited here. For example, the running step size of the self-learning program can preferably be 1e. -6 s.
[0032] This application determines the judgment results of motor self-learning faults, including timeout faults, overspeed faults, stall faults, and current over-limit faults, by using one or more of the target parameters, including real-time motor speed, target speed, real-time quadrature-axis current, real-time direct-axis current, and the running time of the self-learning phase. By introducing more dimensions of parameters (speed, current, duration, etc.) to judge motor self-learning faults, this application can more accurately and comprehensively identify possible fault problems during the motor self-learning process compared to the traditional single speed deviation judgment method, thereby achieving more accurate fault judgment.
[0033] Furthermore, in one embodiment, reference is made to Figure 2 As shown, determining the judgment result of the motor self-learning fault based on one or more parameters in the target parameters includes: Step S10: Determine whether there is a self-learning timeout fault based on the runtime of the self-learning phase and the preset first duration threshold; Step S20: Determine whether a self-learning overspeed fault exists based on the real-time motor speed, target speed, and preset second duration threshold. Step S30: Determine whether a self-learning stall fault exists based on the real-time motor speed, target speed, preset speed threshold, preset speed difference threshold, and preset third duration threshold; Step S40: Determine whether there is a self-learning current over-limit fault based on the real-time direct-axis current, real-time quadrature-axis current, preset current threshold, and preset fourth duration threshold.
[0034] In an exemplary embodiment of this application, the target parameters include real-time motor speed, target speed, real-time quadrature-axis current, real-time direct-axis current, and the running duration of the self-learning phase. The specific values of the preset first duration threshold, preset second duration threshold, preset third duration threshold, preset fourth duration threshold, preset speed threshold, preset speed difference threshold, and preset current threshold can be determined according to actual needs and are not limited here. For example, the preset first duration threshold can preferably be 1s, the preset second duration threshold can preferably be 0.002s, the preset third duration threshold can preferably be 0.002s, the preset fourth duration threshold can preferably be 0.002s, the preset speed threshold can preferably be 10rpm, and the preset speed difference threshold can preferably be 20rpm.
[0035] It should be noted that when performing motor self-learning fault detection, one or more types of motor self-learning faults can be detected. In this embodiment, multiple faults are detected simultaneously. Specifically, the existence of a self-learning timeout fault can be determined based on the relationship between the runtime of the self-learning stage and a preset first duration threshold. If the runtime of each stage is not greater than the preset first duration threshold, it indicates that the motor does not have a self-learning timeout fault. If the runtime of any stage is greater than the preset first duration threshold, it indicates that the motor has a self-learning timeout fault. The existence of a self-learning overspeed fault can be accurately determined based on the relationship between the real-time motor speed and the target speed, and the relationship between the duration of the real-time motor speed being greater than the target speed and a preset second duration threshold. If the real-time motor speed is greater than the target speed and the duration of the real-time motor speed being greater than the target speed is greater than the preset second duration threshold, it indicates that the motor has a self-learning overspeed fault. If the real-time motor speed is not greater than the target speed or the duration of the real-time motor speed being greater than the target speed is not greater than the preset second duration threshold, it indicates that the motor does not have a self-learning timeout fault.
[0036] It should be noted that the determination of whether a motor has a self-learning stall fault is based on the relationship between the real-time motor speed and a preset speed threshold, the relationship between the difference between the real-time motor speed and the target speed and a preset speed difference threshold, and the relationship between the duration for which the difference is greater than the preset speed difference threshold and a preset third duration threshold. Specifically, if the real-time motor speed is not greater than the preset speed threshold, the difference between the real-time motor speed and the target speed is greater than the preset speed difference threshold, and the duration for which the difference is greater than the preset third duration threshold, it indicates that the motor has a self-learning stall fault. If any one of the following conditions is met: the real-time motor speed is greater than the preset speed threshold, the difference between the real-time motor speed and the target speed is not greater than the preset speed difference threshold, or the duration for which the difference is greater than the preset speed difference threshold is not greater than the preset third duration threshold, it indicates that the motor does not have a self-learning stall fault.
[0037] Understandably, the system determines whether a motor has an overcurrent fault by combining the relationship between the real-time direct-axis current and real-time quadrature-axis current and a preset current threshold, as well as the relationship between the duration of this relationship and a preset fourth duration threshold. Specifically, a target current is determined based on the real-time direct-axis current and real-time quadrature-axis current. If the target current is greater than the preset current threshold and its duration is greater than the preset fourth duration threshold, the motor has an overcurrent fault. If the target current is not greater than the preset current threshold, or if the duration of the target current being greater than the preset current threshold is not greater than the preset fourth duration threshold, the motor does not have an overcurrent fault. In summary, through these comprehensive judgments, the system can effectively identify various faults that may occur during the motor's self-learning process, ensuring that the motor can complete the self-learning task smoothly and stably.
[0038] Further, in one embodiment, the target parameters include the runtime of the self-learning phase, the self-learning phase including a phase for acquiring the initial motor angle, a phase for acquiring the reference current, and a phase for acquiring the angle compensation value, and the determination of the judgment result of the motor self-learning fault based on one or more parameters in the target parameters includes: Determine whether the runtime of each stage in the self-learning phase is not greater than the preset first duration threshold. If so, the result of the self-learning fault judgment is that there is no self-learning timeout fault; If not, the result of the self-learning fault judgment is that there is a self-learning timeout fault.
[0039] In an exemplary embodiment of this application, the target parameter includes the runtime of the self-learning phase, which includes a phase for acquiring the initial motor angle, a phase for acquiring the reference current, and a phase for acquiring the angle compensation value. When determining motor self-learning faults, in addition to detecting multiple fault types simultaneously, it is also possible to detect only self-learning timeout faults. Specifically, the runtime of each phase during the motor self-learning phase is monitored in real time and compared with a preset first time threshold. If the runtime of all phases is not greater than the preset first time threshold, it indicates that the self-learning process proceeds smoothly according to the predetermined time, and the determination result of the self-learning fault is that there is no self-learning timeout fault. Conversely, if the runtime of a certain phase is greater than the preset first time threshold, it indicates that the motor failed to complete the task of that phase within the specified time, and the determination result of the self-learning fault is that there is a self-learning timeout fault.
[0040] Further, in one embodiment, the target parameters include real-time motor speed and target speed, and determining the judgment result of motor self-learning fault based on one or more of the target parameters includes: Determine whether the real-time motor speed is greater than the target speed and whether the duration for which the real-time motor speed is greater than the target speed is greater than a preset second duration threshold. If so, the result of the self-learning fault judgment is that there is a self-learning overspeed fault; If not, the result of the self-learning fault determination is that there is no self-learning overspeed fault.
[0041] In this embodiment of the application, the target parameters include the real-time motor speed and the target speed. When determining motor self-learning faults, in addition to detecting multiple fault types simultaneously, it is also possible to detect only self-learning overspeed faults. Specifically, during the motor self-learning process, it is determined whether the motor speed is greater than the target speed. If the real-time motor speed is greater than the target speed, the duration of this state is checked to see if it exceeds a preset second duration threshold. If the duration exceeds the preset second duration threshold, it indicates that the motor speed is too high, which may lead to system instability or overload. In this case, the self-learning fault determination result is that a self-learning overspeed fault exists. Conversely, if the real-time motor speed is not greater than the target speed, or if the duration of the real-time motor speed being greater than the target speed is not greater than the preset second duration threshold, it indicates that the motor speed is within the expected range, ensuring that the self-learning process proceeds according to the set target. In this case, the self-learning fault determination result is that no self-learning overspeed fault exists. Through precise monitoring and determination of these parameters, the system can effectively detect and prevent motor overspeed problems, ensuring the stability and reliability of the self-learning process.
[0042] Furthermore, in one embodiment, the target parameters include the real-time motor speed and the target speed, and the determination of the motor self-learning fault judgment result based on one or more of the target parameters further includes: The speed difference is determined based on the real-time motor speed and the target speed; Determine whether the speed difference is greater than a preset difference threshold, whether the duration for which the speed difference is greater than the preset difference threshold is greater than a preset third duration threshold, and whether the real-time motor speed is not greater than a preset speed threshold. If so, the result of the self-learning fault judgment is that there is a self-learning stall fault; If not, the result of the self-learning fault determination is that there is no self-learning stall fault.
[0043] As an example, in this embodiment of the application, the target parameters include the real-time motor speed and the target speed. The speed difference is obtained by substituting the real-time motor speed and the target speed into the following calculation formula:
[0044] In the formula, This is the speed difference; The target rotational speed; This refers to the real-time motor speed. It should be noted that, to ensure the speed difference is positive, the maximum value between the real-time motor speed and the target speed can be subtracted from the minimum value, or the absolute value of the difference can be used to represent the speed difference. .
[0045] Specifically, when judging motor self-learning faults, in addition to detecting multiple fault types simultaneously, it is also possible to detect only self-learning stall faults. Specifically, during the motor self-learning process, it is determined whether the speed difference is greater than a preset difference threshold. If the speed difference is greater than the preset difference threshold, it is further determined whether the duration of the speed difference being greater than the preset difference threshold is greater than a preset third duration threshold. If the duration is greater than the preset third duration threshold, it indicates that a stall phenomenon exists, that is, the motor cannot rotate normally or is severely stalled. The judgment result of the self-learning fault is that a self-learning stall fault exists.
[0046] Understandably, if the speed difference is not greater than the preset difference threshold, or if the duration of the speed difference being greater than the preset difference threshold is not greater than the preset third duration threshold, it indicates that the motor speed variation is within the normal range and can successfully complete the self-learning process. In this case, the self-learning fault judgment result is that there is no self-learning stall fault. Through this multi-threshold judgment, the system can accurately identify whether the motor has a stall fault, ensuring that the self-learning process is stable and effective.
[0047] Further, in one embodiment, the target parameters include real-time direct-axis current and real-time quadrature-axis current, and determining the judgment result of the motor self-learning fault based on one or more of the target parameters includes: The target current is determined based on the real-time direct-axis current and the real-time quadrature-axis current. Determine whether the target current is greater than a preset current threshold and whether the duration for which the target current is greater than the preset current threshold is greater than a preset fourth duration threshold. If so, the result of the self-learning fault judgment is that there is a self-learning current over-limit fault; If not, the result of the self-learning fault judgment is that there is no self-learning current over-limit fault.
[0048] As an example, in this embodiment of the application, the target parameters include real-time direct-axis current and real-time quadrature-axis current. The target current is obtained by substituting the real-time direct-axis current and real-time quadrature-axis current into the following calculation formula:
[0049] In the formula, This is the real-time direct-axis current; This refers to the real-time quadrature-axis current. The target current.
[0050] Understandably, when performing motor self-learning fault judgment, in addition to detecting multiple fault types simultaneously, it is also possible to detect only the self-learning current over-limit fault. Specifically, after obtaining the target current, it is determined whether the target current is greater than a preset current threshold. If the target current is greater than the preset current threshold, it is further determined whether the duration for which the target current is greater than the preset current threshold is greater than a preset fourth duration threshold. If the duration for which the target current is greater than the preset current threshold is greater than the preset fourth duration threshold, it indicates that the motor may be experiencing excessive current due to overload or other problems, which may lead to a current over-limit fault. In this case, the self-learning fault judgment result is determined to be a self-learning current over-limit fault.
[0051] It should be noted that if the target current is not greater than the preset current threshold or the duration for which the target current is greater than the preset current threshold is not greater than the preset fourth duration threshold, it indicates that the current change is within the normal range, meaning that the motor will not experience overload or abnormal current increase. In this case, the self-learning fault judgment result is that there is no self-learning current over-limit fault. Through accurate judgment of current parameters, the system can promptly detect and handle current over-limit problems, ensuring the safe and stable operation of the motor during the self-learning process.
[0052] Secondly, embodiments of this application also provide a system for determining motor self-learning faults, the system comprising: The judgment module is used to determine the judgment result of the motor self-learning fault based on one or more of the target parameters. The judgment result includes timeout fault, overspeed fault, stall fault and current over-limit fault. The target parameters include real-time motor speed, target speed, real-time quadrature-axis current, real-time direct-axis current and running time of the self-learning stage.
[0053] Furthermore, in one embodiment, the determination module is specifically used for: The system determines whether a self-learning timeout fault exists based on the runtime of the self-learning phase and a preset first duration threshold. The system determines whether a self-learning overspeed fault exists based on the real-time motor speed, the target speed, and the preset second duration threshold. The presence of a self-learning stall fault is determined based on real-time motor speed, target speed, preset speed threshold, preset speed difference threshold, and preset third duration threshold. The system determines whether a self-learning current over-limit fault exists based on the real-time direct-axis current, real-time quadrature-axis current, preset current threshold, and preset fourth duration threshold.
[0054] Furthermore, in one embodiment, the target parameter includes the runtime of the self-learning phase, which includes a phase for acquiring the initial motor angle, a phase for acquiring the reference current, and a phase for acquiring the angle compensation value. The judgment module is further specifically used for: Determine whether the runtime of each stage in the self-learning phase is not greater than the preset first duration threshold. If so, the result of the self-learning fault judgment is that there is no self-learning timeout fault; If not, the result of the self-learning fault judgment is that there is a self-learning timeout fault.
[0055] Furthermore, in one embodiment, the target parameter includes the real-time motor speed and the target speed, and the judgment module is further used for: Determine whether the real-time motor speed is greater than the target speed and whether the duration for which the real-time motor speed is greater than the target speed is greater than a preset second duration threshold. If so, the result of the self-learning fault judgment is that there is a self-learning overspeed fault; If not, the result of the self-learning fault determination is that there is no self-learning overspeed fault.
[0056] Furthermore, in one embodiment, the target parameter includes the real-time motor speed and the target speed, and the judgment module is further used for: The speed difference is determined based on the real-time motor speed and the target speed; Determine whether the speed difference is greater than a preset difference threshold, whether the duration for which the speed difference is greater than the preset difference threshold is greater than a preset third duration threshold, and whether the real-time motor speed is not greater than a preset speed threshold. If so, the result of the self-learning fault judgment is that there is a self-learning stall fault; If not, the result of the self-learning fault determination is that there is no self-learning stall fault.
[0057] Furthermore, in one embodiment, the target parameters include real-time direct-axis current and real-time quadrature-axis current, and the judgment module is further used for: The target current is determined based on the real-time direct-axis current and the real-time quadrature-axis current. Determine whether the target current is greater than a preset current threshold and whether the duration for which the target current is greater than the preset current threshold is greater than a preset fourth duration threshold. If so, the result of the self-learning fault judgment is that there is a self-learning current over-limit fault; If not, the result of the self-learning fault judgment is that there is no self-learning current over-limit fault.
[0058] The functions of each module in the above-mentioned motor self-learning fault judgment system correspond to the steps in the above-mentioned motor self-learning fault judgment method embodiment, and their functions and implementation processes will not be described in detail here.
[0059] Thirdly, embodiments of this application provide a device for determining motor self-learning faults. The device for determining motor self-learning faults can be a personal computer (PC), a laptop computer, a server, or other devices with data processing capabilities.
[0060] Reference Figure 3 , Figure 3 This is a schematic diagram of the hardware structure of the motor self-learning fault determination device involved in the embodiments of this application. In the embodiments of this application, the motor self-learning fault determination device may include a processor, a memory, a communication interface, and a communication bus.
[0061] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.
[0062] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting internal components of the motor self-learning fault diagnosis device, as well as interfaces used for interconnecting the motor self-learning fault diagnosis device with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.
[0063] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.
[0064] The processor can be a general-purpose processor, which can call the motor self-learning fault judgment program stored in the memory and execute the motor self-learning fault judgment method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the motor self-learning fault judgment program is called can refer to the various embodiments of the motor self-learning fault judgment method of this application, and will not be repeated here.
[0065] Those skilled in the art will understand that Figure 3 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0066] Fourthly, embodiments of this application also provide a readable storage medium.
[0067] The present application has a readable storage medium storing a motor self-learning fault judgment program, wherein when the motor self-learning fault judgment program is executed by a processor, the steps of the motor self-learning fault judgment method described above are implemented.
[0068] The method implemented when the motor self-learning fault judgment program is executed can be referred to in the various embodiments of the motor self-learning fault judgment method of this application, and will not be repeated here.
[0069] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.
[0070] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.
[0071] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.
[0072] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.
[0073] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0074] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.
[0075] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.
Claims
1. A method of judging a motor self-learning failure, characterized by, The motor self-learning fault judgment method comprises: The motor self-learning fault judgment result is determined based on one or more parameters in the target parameters, the judgment result comprises an overtime fault, an overspeed fault, a locked-rotor fault and a current overrun fault, and the target parameters comprise a real-time motor speed, a target speed, a real-time quadrature-axis current, a real-time direct-axis current and a running time length in a self-learning stage.
2. The method of claim 1, wherein the step of determining the fault of the motor is performed by a microcomputer. The motor self-learning fault judgment result is determined based on one or more parameters in the target parameters, the judgment result comprises an overtime fault, an overspeed fault, a locked-rotor fault and a current overrun fault, and the target parameters comprise a real-time motor speed, a target speed, a real-time quadrature-axis current, a real-time direct-axis current and a running time length in a self-learning stage. The motor self-learning fault judgment result is determined based on one or more parameters in the target parameters, the judgment result comprises an overtime fault, an overspeed fault, a locked-rotor fault and a current overrun fault, and the target parameters comprise a real-time motor speed, a target speed, a real-time quadrature-axis current, a real-time direct-axis current and a running time length in a self-learning stage. The motor self-learning fault judgment result is determined based on one or more parameters in the target parameters, the judgment result comprises an overtime fault, an overspeed fault, a locked-rotor fault and a current overrun fault, and the target parameters comprise a real-time motor speed, a target speed, a real-time quadrature-axis current, a real-time direct-axis current and a running time length in a self-learning stage. The motor self-learning fault judgment result is determined based on one or more parameters in the target parameters, the judgment result comprises an overtime fault, an overspeed fault, a locked-rotor fault and a current overrun fault, and the target parameters comprise a real-time motor speed, a target speed, a real-time quadrature-axis current, a real-time direct-axis current and a running time length in a self-learning stage. The motor self-learning fault judgment result is determined based on one or more parameters in the target parameters, the judgment result comprises an overtime fault, an overspeed fault, a locked-rotor fault and a current overrun fault, and the target parameters comprise a real-time motor speed, a target speed, a real-time quadrature-axis current, a real-time direct-axis current and a running time length in a self-learning stage.
3. The method of claim 1, wherein the step of determining the fault of the motor is performed by a microcomputer. The motor self-learning fault judgment result is determined based on one or more parameters in the target parameters, the judgment result comprises an overtime fault, an overspeed fault, a locked-rotor fault and a current overrun fault, and the target parameters comprise a real-time motor speed, a target speed, a real-time quadrature-axis current, a real-time direct-axis current and a running time length in a self-learning stage. The motor self-learning fault judgment result is determined based on one or more parameters in the target parameters, the judgment result comprises an overtime fault, an overspeed fault, a locked-rotor fault and a current overrun fault, and the target parameters comprise a real-time motor speed, a target speed, a real-time quadrature-axis current, a real-time direct-axis current and a running time length in a self-learning stage. The motor self-learning fault judgment result is determined based on one or more parameters in the target parameters, the judgment result comprises an overtime fault, an overspeed fault, a locked-rotor fault and a current overrun fault, and the target parameters comprise a real-time motor speed, a target speed, a real-time quadrature-axis current, a real-time direct-axis current and a running time length in a self-learning stage. The motor self-learning fault judgment result is determined based on one or more parameters in the target parameters, the judgment result comprises an overtime fault, an overspeed fault, a locked-rotor fault and a current overrun fault, and the target parameters comprise a real-time motor speed, a target speed, a real-time quadrature-axis current, a real-time direct-axis current and a running time length in a self-learning stage.
4. The method of claim 1, wherein the step of determining the fault of the motor is performed by a microcomputer. The motor self-learning fault judgment result is determined based on one or more parameters in the target parameters, the judgment result comprises an overtime fault, an overspeed fault, a locked-rotor fault and a current overrun fault, and the target parameters comprise a real-time motor speed, a target speed, a real-time quadrature-axis current, a real-time direct-axis current and a running time length in a self-learning stage. The motor self-learning fault judgment result is determined based on one or more parameters in the target parameters, the judgment result comprises an overtime fault, an overspeed fault, a locked-rotor fault and a current overrun fault, and the target parameters comprise a real-time motor speed, a target speed, a real-time quadrature-axis current, a real-time direct-axis current and a running time length in a self-learning stage. The motor self-learning fault judgment result is determined based on one or more parameters in the target parameters, the judgment result comprises an overtime fault, an overspeed fault, a locked-rotor fault and a current overrun fault, and the target parameters comprise a real-time motor speed, a target speed, a real-time quadrature-axis current, a real-time direct-axis current and a running time length in a self-learning stage. The motor self-learning fault judgment result is determined based on one or more parameters in the target parameters, the judgment result comprises an overtime fault, an overspeed fault, a locked-rotor fault and a current overrun fault, and the target parameters comprise a real-time motor speed, a target speed, a real-time quadrature-axis current, a real-time direct-axis current and a running time length in a self-learning stage.
5. The method of claim 1, wherein the step of determining the fault of the motor is performed by a self-learning algorithm. 6. The method of claim 1, wherein the step of determining the fault of the motor is performed by a microcomputer. determining whether the target current is greater than a preset current threshold and whether a duration for which the target current is greater than the preset current threshold is greater than a preset fourth duration threshold; if yes, determining that the result of the self-learning fault determination is that a self-learning current overrun fault exists; if no, determining that the result of the self-learning fault determination is that a self-learning current overrun fault does not exist.
7. A system for determining a fault of a motor self-learning, characterized by The motor self-learning fault determination system comprises: a determination module configured to determine a result of motor self-learning fault determination based on one or more of target parameters, the result of the motor self-learning fault determination comprising an overtime fault, an overspeed fault, a locked-rotor fault, and a current overrun fault, the target parameters comprising a real-time motor speed, a target speed, a real-time quadrature-axis current, a real-time direct-axis current, and a running duration of a self-learning phase.
8. The system for judging a motor self-learning failure according to claim 7, wherein The determination module is specifically configured to: determine whether a self-learning overtime fault exists based on the running duration of the self-learning phase and a preset first duration threshold; determine whether a self-learning overspeed fault exists according to the real-time motor speed, the target speed, and a preset second duration threshold; determine whether a self-learning locked-rotor fault exists based on the real-time motor speed, the target speed, a preset speed threshold, a preset speed difference threshold, and a preset third duration threshold; determine whether a self-learning current overrun fault exists according to the real-time direct-axis current, the real-time quadrature-axis current, a preset current threshold, and a preset fourth duration threshold.
9. A device for determining self-learning faults in motors, characterized in that, The motor self-learning fault determination device comprises a processor, a memory, and a motor self-learning fault determination program stored on the memory and executable by the processor, wherein the motor self-learning fault determination program, when executed by the processor, implements the steps of the motor self-learning fault determination method according to any one of claims 1 to 6.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a motor self-learning fault determination program, wherein the motor self-learning fault determination program, when executed by a processor, implements the steps of the motor self-learning fault determination method according to any one of claims 1 to 6.