A rudder performance parameter testing system

Abstract

This application discloses a servo motor performance parameter testing system, relating to the technical field of performance testing. It includes a dynamic load simulation module for simulating dynamic electromechanical conditions of the servo motor; a micro-feature extraction module for acquiring real-time torque feedback data and obtaining torque pulsation characteristics based on the data; a concurrent crosstalk monitoring module for obtaining system-level crosstalk data caused by back electromotive force; a thermal effect tracking module for monitoring the real-time temperature of the servo motor and generating zero-point thermal drift data; a communication impairment injection module for monitoring and extracting runaway protection response data of the servo motor under communication obstruction conditions; and a comprehensive evaluation and prediction module for constructing a health assessment model and generating a quality consistency review report and equipment degradation early warning information based on the model. This application improves the comprehensiveness and accuracy of servo motor performance testing.

Description

Technical Field

[0001] This application relates to the technical field of performance testing, and in particular to a servo motor performance parameter testing system. Background Technology

[0002] As a type of position servo drive control device, servo motors are widely used in industrial automation, Internet of Things control, and aerospace fields. The accuracy and stability of their performance parameters are directly related to the operational precision and safety of the entire complex control system.

[0003] In existing technologies, performance parameter testing of servos is often limited to static verification by sending ideal waveforms under no-load or constant load conditions. This makes it difficult to realistically simulate the sudden changes and nonlinear dynamic damping loads faced by the equipment in complex industrial scenarios, and also fails to effectively extract internal microscopic torque pulsation characteristics, resulting in a severe disconnect between the test environment and actual harsh operating conditions. On the other hand, existing test systems, when conducting multi-channel joint tests, typically ignore the back electromotive force and system bus crosstalk issues generated by the concurrent operation of multiple servos, and lack the ability to continuously monitor zero-point drift caused by heat accumulation effects during long-term operation. Furthermore, traditional testing mainly focuses on the electromechanical physics level, rarely incorporating boundary interference tests such as external communication network delays or packet loss. The acquired test data has a relatively single dimension, making it impossible to comprehensively assess the overall health of the servo, let alone provide early warning of equipment degradation.

[0004] Therefore, how to accurately and comprehensively conduct multi-dimensional performance tests on servos has become an urgent problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a servo motor performance parameter testing system to solve the problems mentioned in the background art.

[0006] This application provides a servo motor performance parameter testing system, the system comprising: Dynamic load simulation module: used to acquire the real-time operating parameters of the servo motor, and apply a nonlinear follow-up damping load to the test bench according to the real-time operating parameters to simulate the dynamic electromechanical conditions of the servo motor. Microscopic feature extraction module: used to acquire test waveform commands, link the test waveform commands with the follower damping load to trigger transient resistance impact, and collect real-time torque feedback data, and obtain torque pulsation characteristics based on the real-time torque feedback data; Concurrent crosstalk monitoring module: used to send multi-channel concurrent commands to make the servos of multiple channels run simultaneously under the dynamic electromechanical conditions, synchronously collect the voltage and current waveforms of the system bus, and obtain system-level crosstalk data caused by back electromotive force based on the voltage and current waveforms; Thermal effect tracking module: used to monitor the real-time temperature of the servo motor. When the real-time temperature reaches the preset temperature threshold, a zero-point query command is sent to obtain and compare the current mechanical absolute zero point with the initial electrical zero point to generate zero-point thermal drift data. Communication impairment injection module: used to inject a preset communication impairment signal into the bus protocol configuration layer under the dynamic electromechanical conditions, and to monitor and extract the runaway protection response data of the servo motor under communication obstruction. The comprehensive assessment and prediction module is used to fuse the torque pulsation characteristics, the system-level crosstalk data, the zero-point thermal drift data, and the runaway protection response data to construct a health assessment model, and generate a quality consistency review report and equipment degradation early warning information based on the health assessment model.

[0007] Preferably, the dynamic load simulation module includes: Real-time parameter acquisition unit and dynamic damping control unit; Real-time parameter acquisition unit: After the test system is initialized and a communication connection is established with the servo under test, it can acquire the current angular velocity data and current commanded rotation angle data of the servo under no-load or constant-load conditions in real time. The current angular velocity data and the current commanded rotation angle data are combined to generate the real-time operating parameters of the servo motor, and the real-time operating parameters are sent to the dynamic damping control unit. Dynamic damping control unit: used to receive the real-time operating parameters, perform model matching in a preset fluid resistance model library based on the real-time operating parameters, and extract the corresponding nonlinear damping coefficient; Based on the aforementioned nonlinear damping coefficient, a sudden, dynamically changing follow-up damping load is applied to the servo motor to simulate the dynamic electromechanical conditions of the servo motor in harsh industrial scenarios.

[0008] Preferably, the microscopic feature extraction module includes: Waveform instruction configuration unit and torque pulsation analysis unit; Waveform command configuration unit: used to acquire the preset sine wave test waveform command issued by the host computer software, and generate a trigger signal when the test waveform command runs to the peak or trough node with the maximum acceleration; The trigger signal is sent to the dynamic load simulation module, and the test waveform command is linked with the follow-up damping load to generate transient drag impact on the servo motor under test. Torque pulsation analysis unit: used to collect torque data inside the servo motor at a preset ultra-high frequency acquisition speed at the instant when the transient resistance impact occurs, to obtain real-time torque feedback data, and to plot the real-time torque feedback data into a high-frequency torque curve; The high-frequency torque curve is filtered to remove the basic mechanical torque portion, thus obtaining the high-frequency pulsating torque characteristics.

[0009] Preferably, the concurrent crosstalk monitoring module includes: Concurrent instruction scheduling unit and bus waveform analysis unit; Concurrent command scheduling unit: used to send multi-channel concurrent commands to the servo under test on multiple channels simultaneously according to the user's multi-channel selection operation; This allows the tested servos in multiple test channels to operate simultaneously under the dynamic electromechanical conditions and synchronously perform emergency stop or instantaneous reversing actions under extreme conditions to generate the maximum transient current. Bus waveform analysis unit: used to collect voltage and current waveform data on the test bench system bus when the servo under test performs emergency stop or instantaneous reversing action on multiple channels; The voltage waveform data and the current waveform data are anomaly identified to obtain abnormal ripple characteristics. Based on the abnormal ripple characteristics, the bus ripple mutation data generated by the back electromotive force generated by the servo motor is recorded, and system-level crosstalk data is obtained from the bus ripple mutation data.

[0010] Preferably, the thermal effect tracking module includes: Real-time temperature monitoring unit and zero-point drift calculation unit; Real-time temperature monitoring unit: used to monitor the real-time temperature of the casing and the internal motor of the servo under test after the servo has undergone long-term high-intensity concurrent operation in multiple channels. Determine whether the real-time temperature of the outer casing and / or the real-time temperature of the internal motor reaches or exceeds a preset heating temperature threshold. If it is determined that the heating temperature threshold has been reached or exceeded, the current dynamic test is interrupted and a zero-point query command is sent to the zero-point drift calculation unit. Zero-point drift calculation unit: After receiving the zero-point query command, it controls the tested servo to perform a return-to-zero action and collects the mechanical absolute zero-point data of the tested servo after returning to zero; The mechanical absolute zero position data is compared with the initial electrical zero position recorded by the tested servo motor in a cold state to obtain the physical deformation deviation data caused by heat accumulation. Zero point thermal drift data is generated based on the physical deformation deviation data.

[0011] Preferably, the communication impairment injection module includes: Damage signal generation unit and runaway response extraction unit; Damage signal generation unit: used to randomly generate communication damage signals, including communication packet loss, message out-of-order and extreme command delay, within the bus protocol configuration layer during the operation of the tested servo motor under the dynamic electromechanical conditions. The communication impairment signal is continuously injected into the network data stream that is communicating with the servo under test, so that the underlying controller of the servo under test is in an extremely harsh state where the electromechanical environment and communication network are blocked. Runaway response extraction unit: used to monitor and extract the runaway protection response data of the underlying controller of the servo under test in real time when the servo under test is in the extreme severe state; The runaway protection response data specifically includes the precise delay time of the tested servo triggering the power-off protection mechanism, the torque maintenance data in the automatic braking lock-up state, and the system automatic reconnection time after network communication is restored.

[0012] Preferably, the comprehensive evaluation and prediction module includes: Multidimensional feature fusion unit and health assessment unit; Multi-dimensional feature fusion unit: used to extract the torque pulsation features, system-level crosstalk data, zero-point thermal drift data and runaway protection response data obtained in the test process, and use them as the multi-dimensional state representation vector of the device; The multidimensional state representation vector is normalized and cross-dimensional deep feature fusion is performed to generate a standard input dataset. Health assessment unit: used to input the standard input dataset into a pre-set long short-term memory network for deduction, and construct a multi-dimensional health assessment model of the tested servo motor; Based on the multi-dimensional health assessment model, the health index and pass rate evaluation of the tested servo motor are output. Combining the health index and the pass rate evaluation, a quality consistency report is generated.

[0013] Preferably, the comprehensive evaluation and prediction module further includes: Degradation early warning push unit; Degradation warning push unit: used to predict the life degradation of the core components of the tested servo motor based on the health index and the pass rate evaluation, and obtain the predicted life degradation information. Based on the predicted lifespan decay information, the remaining lifespan curve of the core components of the tested servo motor is constructed, and the decay trend of the remaining lifespan curve is extracted. By combining the remaining lifespan curve and the degradation trend, device degradation information of the tested servo motor is generated; Based on the equipment degradation information, the predicted remaining lifespan and failure type of the tested servo motor are obtained, and the severity level is classified according to the predicted remaining lifespan and failure type to obtain the target hazard level. Based on the target hazard level, an early warning message is generated and sent to maintenance personnel.

[0014] In summary, this application includes at least one of the following beneficial technical effects: By acquiring the real-time operating parameters of the servo motors, a nonlinear servo-damped load is applied to the test bench to simulate a realistic dynamic electromechanical condition. Then, the test waveform command is linked with the servo-damped load to trigger a transient drag impact, and real-time torque feedback data is collected under this state to extract torque pulsation characteristics reflecting the internal properties of the servo motors. Next, multi-channel concurrent commands are sent to simultaneously operate multiple servo motors under dynamic electromechanical conditions, synchronously acquiring the voltage and current waveforms of the system bus to obtain system-level crosstalk data caused by back electromotive force; simultaneously, the real-time temperature of the servo motors is monitored, and when a preset temperature threshold is reached, the current mechanical absolute zero position is acquired and compared with the initial electrical zero position to generate zero-point thermal drift data. Furthermore, under dynamic electromechanical conditions, communication impairment signals are injected into the bus protocol configuration layer to monitor and extract runaway protection response data of the servo motors under dual electromechanical and communication obstruction states. Finally, the torque pulsation characteristics, system-level crosstalk data, zero-point thermal drift data, and runaway protection response data are fused across dimensions to construct a health assessment model. Based on this model, a quality consistency review report and equipment degradation early warning information are generated. This improves the comprehensiveness and accuracy of servo motor performance testing. Attached Figure Description

[0015] Figure 1 This is a block diagram of a servo motor performance parameter testing system provided in an embodiment of this application.

[0016] Figure labeling: 1. Dynamic load simulation module; 2. Microscopic feature extraction module; 3. Concurrent crosstalk monitoring module; 4. Thermal effect tracking module; 5. Communication impairment injection module; 6. Comprehensive evaluation and prediction module. Detailed Implementation

[0017] The following is in conjunction with the appendix Figure 1 This application will be described in further detail, but the embodiments of the present invention are not limited thereto.

[0018] This application discloses a servo motor performance parameter testing system.

[0019] In this embodiment, a servo motor performance parameter testing system is provided, the system comprising: Dynamic load simulation module 1: Used to acquire the real-time operating parameters of the servo motor, and apply a nonlinear follow-up damping load to the test bench according to the real-time operating parameters to simulate the dynamic electromechanical conditions of the servo motor. Microscopic feature extraction module 2: used to acquire test waveform commands, link the test waveform commands with the follower damping load to trigger transient resistance impact, and collect real-time torque feedback data, and obtain torque pulsation characteristics based on the real-time torque feedback data; Concurrent crosstalk monitoring module 3: Used to send multi-channel concurrent commands to enable multiple channels of servo motors to operate simultaneously under dynamic electromechanical conditions, synchronously collect voltage and current waveforms of the system bus, and obtain system-level crosstalk data caused by back electromotive force based on the voltage and current waveforms; Thermal effect tracking module 4: Used to monitor the real-time temperature of the servo motor. When the real-time temperature reaches the preset temperature threshold, it sends a zero-point query command to obtain and compare the current mechanical absolute zero point with the initial electrical zero point and generate zero-point thermal drift data. Communication Impairment Injection Module 5: Used to inject preset communication impairment signals into the bus protocol configuration layer under dynamic electromechanical conditions, monitor and extract runaway protection response data of the servo motor under communication obstruction conditions; Comprehensive assessment and prediction module 6: It is used to fuse torque pulsation characteristics, system-level crosstalk data, zero-point thermal drift data and runaway protection response data to build a health assessment model, and generate a quality consistency review report and equipment degradation early warning information based on the health assessment model.

[0020] The dynamic load simulation module includes: Real-time parameter acquisition unit and dynamic damping control unit; Real-time parameter acquisition unit: After the test system is initialized and a communication connection is established with the servo under test, it can acquire the current angular velocity data and current commanded rotation angle data of the servo under no-load or constant-load conditions in real time. The current angular velocity data and the current commanded rotation angle data are combined to generate the real-time operating parameters of the servo motor, and the real-time operating parameters are sent to the dynamic damping control unit. Dynamic damping control unit: used to receive real-time operating parameters, perform model matching in a preset fluid resistance model library based on the real-time operating parameters, and extract the corresponding nonlinear damping coefficient; Based on the nonlinear damping coefficient, a sudden, dynamically changing follower damping load is applied to the servo motor to simulate the dynamic electromechanical conditions of the servo motor in harsh industrial scenarios.

[0021] In practice, we'll take an SD-500 industrial servo motor as an example. First, the test system starts up and successfully establishes a communication connection with the servo motor. Then, the real-time parameter acquisition unit begins working, reading data from the servo motor controller. This unit obtains the current angular velocity data of the servo motor under no-load conditions, such as 120 revolutions per minute, and also obtains the currently received commanded rotation angle data, such as a request to rotate to a 45-degree position. Next, this unit combines the angular velocity data and rotation angle data to generate real-time operating parameters describing the current state of the servo motor, and immediately sends this parameter packet to the dynamic damping control unit. After receiving this real-time operating parameter, the dynamic damping control unit immediately searches and matches it in its internal preset fluid resistance model library. This model library simulates the resistance brought by different media such as oil and air. Based on the received rotation speed and rotation angle information, the unit matches the most suitable fluid model and extracts a nonlinear damping coefficient from it, for example, 0.8. Finally, based on this coefficient of 0.8, the dynamic damping control unit applies a sudden, variable resistance load to the rotating SD-500 servo motor via the load generator on the test bench. This load changes in real time with the servo motor's rotation angle. This successfully simulates the dynamic electromechanical conditions faced by the servo motor when pushing a component through a viscous fluid in a real robotic arm, rather than simply an unloaded or stationary load.

[0022] The micro-feature extraction module includes: Waveform instruction configuration unit and torque pulsation analysis unit; Waveform command configuration unit: used to acquire the preset sine wave test waveform command issued by the host computer software, and generate a trigger signal when the test waveform command runs to the peak or trough node with the maximum acceleration; The trigger signal is sent to the dynamic load simulation module, and the test waveform command is linked with the follow-up damping load to generate transient drag impact on the servo motor under test. Torque pulsation analysis unit: used to collect torque data inside the servo motor at a preset ultra-high frequency acquisition speed at the instant of transient resistance impact, obtain real-time torque feedback data, and plot the real-time torque feedback data into a high-frequency torque curve; By filtering the high-frequency torque curve and removing the basic mechanical torque portion, the high-frequency oscillating torque pulsation characteristics are obtained.

[0023] In practice, let's take the testing of an SD-500 industrial servo as an example. The waveform command configuration unit obtains a preset sine wave test command from the host computer's test software. This command instructs the servo to oscillate back and forth according to a sine curve. Then, the unit closely monitors the operation of this sine wave. When the waveform reaches the peak (or trough) of maximum acceleration, the unit immediately generates a trigger signal. This trigger signal is then immediately sent to the aforementioned dynamic load simulation module. In this way, the test waveform command and the applied servo damping load by the module achieve precise linkage. At the instant of maximum sine wave acceleration, the dynamic load also suddenly increases. The combination of these two factors generates a very strong transient drag impact on the tested SD-500 servo. Simultaneously, the torque pulsation analysis unit begins high-speed operation. In the extremely short instant of this impact, the unit collects data from the servo's built-in torque sensor at a preset ultra-high frequency of 5000 times per second, obtaining a set of real-time torque feedback data. Then, the unit plots these data points into a violently fluctuating high-frequency torque curve. Finally, the unit performs digital filtering on this curve, removing the smooth parts that represent the basic mechanical load. What remains is a series of high-frequency, subtle fluctuation signals. These signals are the torque pulsation characteristics that we want to extract, which can reflect the microscopic states of the servo motor, such as gear clearance and motor commutation.

[0024] The concurrent crosstalk monitoring module includes: Concurrent instruction scheduling unit and bus waveform analysis unit; Concurrent command scheduling unit: used to send multi-channel concurrent commands to the servo under test on multiple channels simultaneously according to the user's multi-channel selection operation; This allows the tested servos in multiple test channels to operate simultaneously under dynamic electromechanical conditions and synchronously perform emergency stops or instantaneous reversals under extreme conditions to generate the maximum transient current. Bus waveform analysis unit: used to collect voltage and current waveform data on the test bench system bus when the servo under test performs emergency stop or instantaneous reversing action on multiple channels; Anomaly identification is performed on voltage and current waveform data to obtain abnormal ripple characteristics. Based on the abnormal ripple characteristics, bus ripple mutation data generated by the back electromotive force generated by the servo motor is recorded, and system-level crosstalk data is obtained from the bus ripple mutation data.

[0025] In this application, we take the testing of an SD-500 industrial servo as an example. Assume the test bench has four channels, simultaneously connecting four identical SD-500 servos. The tester selects these four channels for concurrent testing via software. Then, based on this selection, the concurrent command scheduling unit simultaneously sends a set of identical multi-channel concurrent commands to the servos on all four channels, instructing them to perform rapid reciprocating motion. Thus, the servos on the four test channels simultaneously operate under the previously simulated dynamic electromechanical conditions. Next, during the test, the scheduling unit commands the four servos to synchronously execute an emergency stop under extreme conditions. This action brakes all motors, generating maximum transient current. Simultaneously, the bus waveform analysis unit is activated. At the same moment the servos execute the emergency stop, it begins acquiring voltage and current waveform data on the entire system power supply bus of the test bench. The unit then performs high-speed analysis on this waveform data, identifying abnormal ripple characteristics. Through analysis, the unit discovers several abnormal spikes and drops in the bus voltage. Based on these abnormal ripple characteristics, the unit determines and records that the ripple abrupt change data is caused by the superposition of back electromotive forces generated by the simultaneous emergency stop of the four servo motors, impacting the bus. Finally, based on the amplitude and frequency of these abrupt changes, the unit calculates the electromagnetic interference intensity caused by this concurrent operation to the entire test system, which is the system-level crosstalk data.

[0026] The thermal effect tracking module includes: Real-time temperature monitoring unit and zero-point drift calculation unit; Real-time temperature monitoring unit: used to monitor the real-time temperature of the casing and the internal motor of the servo under test after the servo has undergone long-term high-intensity concurrent operation in multiple channels. Determine whether the real-time temperature of the outer casing and / or the real-time temperature of the internal motor have reached or exceeded the preset heating temperature threshold. If it is determined that the heating temperature threshold has been reached or exceeded, the current dynamic test is interrupted and a zero-point query command is sent to the zero-point drift calculation unit. Zero-point drift calculation unit: After receiving the zero-point query command, it controls the tested servo to perform a return-to-zero action and collects the mechanical absolute zero-point data of the tested servo after returning to zero; The mechanical absolute zero position data is compared with the initial electrical zero position recorded by the tested servo motor in a cold state to obtain the physical deformation deviation data caused by heat accumulation. Zero-point thermal drift data is then generated based on the physical deformation deviation data.

[0027] In practice, let's take the testing of an SD-500 industrial servo as an example. After four SD-500 servos underwent a prolonged, high-intensity concurrent reciprocating motion test, their temperatures began to rise. The real-time temperature monitoring unit was constantly operating, using thermocouples attached to the servo housing and built-in temperature sensors to monitor the real-time housing temperature (e.g., currently 65 degrees Celsius) and the internal motor winding temperature (e.g., currently 85 degrees Celsius) of each tested servo. The unit then compared the monitored temperatures with preset heating temperature thresholds. These thresholds are pre-set safety limits, such as 70 degrees Celsius for the housing and 90 degrees Celsius for the internal motor. The monitoring unit detected that the internal motor temperature of one servo had reached 85 degrees Celsius, very close to the 90-degree threshold. Therefore, for safety and to obtain thermal effect data, the unit immediately interrupted the ongoing dynamic testing process and sent a zero-point query command to the zero-point drift calculation unit. Upon receiving the command, the zero-point drift calculation unit immediately controlled the hotter SD-500 servo to perform a precise return-to-zero maneuver. The servo rotates until its internal mechanical limit switch is triggered. The unit acquires this position as the mechanical absolute zero point data, for example, recorded as 0.05 degrees. Finally, the unit compares this mechanical zero point of 0.05 degrees under hot conditions with the initial electrical zero point (for example, 0.00 degrees) recorded before the test when the servo is cold. The calculated error is 0.05 degrees. This error is the physical deformation deviation caused by the slight expansion and deformation of metal parts due to heat accumulation during long-term operation. Based on this data, the unit generates zero-point thermal drift data of "0.05 degrees".

[0028] The communication impairment injection module includes: Damage signal generation unit and runaway response extraction unit; Damage signal generation unit: used to randomly generate communication damage signals, including communication packet loss, message out-of-order and extreme command delay, within the bus protocol configuration layer during the operation of the tested servo motor under dynamic electromechanical conditions. The communication impairment signal is continuously injected into the network data stream that is communicating with the servo under test, so that the underlying controller of the servo under test is subjected to an extreme and harsh state where the electromechanical environment and communication network are blocked. Runaway response extraction unit: used to monitor and extract runaway protection response data of the underlying controller of the servo under test in real time when the servo under test is in an extreme and severe state; The runaway protection response data specifically includes the precise delay time of the tested servo triggering the power-off protection mechanism, the torque maintenance data in the automatic braking lock-up state, and the system automatic reconnection time after network communication is restored.

[0029] In practice, we take the testing of an SD-500 industrial servo as an example. When an SD-500 servo is continuously running under dynamic electromechanical conditions, the communication impairment injection module activates. The impairment signal generation unit randomly generates a communication impairment signal within the bus protocol configuration layer between the servo and the test host. For example, in this case, it generates an "extreme command delay," which simulates network congestion by intentionally delaying control commands that should be sent to the servo by 100 milliseconds. Then, the unit continuously injects this delayed impairment signal into the real-time network data stream communicating with the servo. This forces the servo's underlying controller to handle both the actual physical load and the severely delayed control commands, placing it in an extreme state where both the electromechanical environment and communication network are blocked. Simultaneously, the runaway response extraction unit closely monitors the servo's underlying controller's response. It monitors and extracts the runaway protection response data of the servo under this extreme condition in real time. Specifically, the data includes: the exact delay time for the servo to trigger the overcurrent power-off protection mechanism after receiving an erroneous timing command, recorded as 15 milliseconds; the torque maintenance data of the mechanical brake device in the locked state after the protective power-off braking, recorded as maintaining a maximum static torque of 3 N·m; and the time required for the servo control system to automatically re-establish connection with the host and initialize after the test host stops injecting damage signals and network communication returns to normal, recorded as 200 milliseconds. These data together constitute the runaway protection response data.

[0030] The comprehensive assessment and prediction module includes: Multidimensional feature fusion unit and health assessment unit; Multi-dimensional feature fusion unit: used to extract torque pulsation features, system-level crosstalk data, zero-point thermal drift data and runaway protection response data obtained in the test process, and use them as the multi-dimensional state representation vector of the equipment; The multidimensional state representation vectors are normalized and cross-dimensional deep feature fusion is performed to generate a standard input dataset. Health assessment unit: used to input the standard input dataset into a pre-set long short-term memory network for deduction and to construct a multi-dimensional health assessment model of the tested servo motor; Based on the multi-dimensional health assessment model, the health index and pass rate evaluation of the tested servo motor are output. Combining the health index and pass rate evaluation, a quality consistency report is generated.

[0031] In practice, taking an SD-500 industrial servo as an example, the comprehensive evaluation and prediction module begins its final analysis after testing an SD-500 servo that has completed all test items. The multi-dimensional feature fusion unit acts first, extracting four key data points generated by previous modules from the entire testing process: torque pulsation characteristics (a set of high-frequency fluctuation values), system-level crosstalk data (electromagnetic interference intensity level 2), zero-point thermal drift data (0.05 degrees), and runaway protection response data (including delay time, holding torque, etc.). Then, the unit uses these four data points as four feature vectors representing the servo's multi-dimensional state. Next, the unit normalizes these vectors, which have different formats and dimensions, scaling them to values ​​between 0 and 1. Finally, the unit uses an algorithm model to perform cross-dimensional deep feature fusion on these normalized data, such as analyzing whether thermal drift affects torque pulsation, generating a standard, unified standard input dataset. The health assessment unit then inputs this standard dataset into a pre-trained long short-term memory network model. This model acts like a brain, deducing and calculating from the input data. Based on the calculations, the model constructs a multi-dimensional health assessment model for the tested SD-500 servo. According to this model, the unit outputs the final assessment result: the servo's overall health index is 92 (out of 100), and its various parameters are rated as "qualified." Combining this health index of 92 and the "qualified" rating, the unit automatically generates a detailed quality consistency report containing specific scores and judgment criteria for each parameter.

[0032] The comprehensive assessment and prediction module also includes: Degradation early warning push unit; Degradation early warning push unit: It is used to predict the life degradation of the core components of the tested servo motor based on the health index and pass rate evaluation, and obtain the predicted life degradation information. Based on the predicted life decay information, the remaining life curve of the core components of the tested servo motor is constructed, and the decay trend of the remaining life curve is extracted. By combining the remaining life curve and the degradation trend, the equipment degradation information of the tested servo motor is generated; Based on the equipment degradation information, the predicted remaining life and failure type of the tested servo motor are obtained, and the severity level is classified according to the predicted remaining life and failure type to obtain the target hazard level. Based on the target hazard level, an early warning message is generated and sent to maintenance personnel.

[0033] In practice, taking an SD-500 industrial servo as an example, the degradation early warning push unit in the comprehensive evaluation and prediction module, after receiving the evaluation result (health index 92, qualified) of a certain SD-500 servo from the health assessment unit, proceeds further. First, based on this health index and combined with the historical life data model of this model of servo, the unit predicts the life degradation of the core components of this servo (such as motor brushes and gear sets), obtaining predicted life degradation information, showing "the gear is expected to wear at a rate 15% higher than the average level." Then, based on this predicted degradation information, the unit constructs the remaining life curve of the core components of this servo in the software. This curve shows that its effective lifespan may decrease from the designed 10,000 hours to about 8,500 hours. Next, the unit extracts the degradation trend of this remaining life curve and finds that the slope of the trend line increases in the later stages. Combining the specific remaining life curve and this accelerated degradation trend, the unit generates a device degradation message: "The equipment gears have an abnormal wear tendency, and the total lifespan is expected to be shortened by 15%." Finally, based on this degradation information, the unit calculated that the servo's predicted remaining lifespan under the current usage intensity is approximately 4200 hours, and determined that the primary failure type is likely "excessive gear wear leading to excessive clearance." Based on this remaining lifespan and failure type, the unit classified the hazard level as "medium" because it is not a sudden, fatal failure. Based on this "medium" target hazard level, the unit generated a warning message: "Servo SD-500-07, estimated remaining lifespan 4200 hours, monitor gear wear, and increase inspection frequency." This warning message was then sent to the maintenance personnel's computer.

[0034] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A servo motor performance parameter testing system, characterized in that, include: Dynamic load simulation module: used to acquire the real-time operating parameters of the servo motor, and apply a nonlinear follow-up damping load to the test bench according to the real-time operating parameters to simulate the dynamic electromechanical conditions of the servo motor. Microscopic feature extraction module: used to acquire test waveform commands, link the test waveform commands with the follower damping load to trigger transient resistance impact, and collect real-time torque feedback data, and obtain torque pulsation characteristics based on the real-time torque feedback data; Concurrent crosstalk monitoring module: used to send multi-channel concurrent commands to make the servos of multiple channels run simultaneously under the dynamic electromechanical conditions, synchronously collect the voltage and current waveforms of the system bus, and obtain system-level crosstalk data caused by back electromotive force based on the voltage and current waveforms; Thermal effect tracking module: used to monitor the real-time temperature of the servo motor. When the real-time temperature reaches the preset temperature threshold, a zero-point query command is sent to obtain and compare the current mechanical absolute zero point with the initial electrical zero point to generate zero-point thermal drift data. Communication impairment injection module: used to inject a preset communication impairment signal into the bus protocol configuration layer under the dynamic electromechanical conditions, and to monitor and extract the runaway protection response data of the servo motor under communication obstruction. The comprehensive assessment and prediction module is used to fuse the torque pulsation characteristics, the system-level crosstalk data, the zero-point thermal drift data, and the runaway protection response data to construct a health assessment model, and generate a quality consistency review report and equipment degradation early warning information based on the health assessment model.

2. The servo motor performance parameter testing system according to claim 1, characterized in that, The dynamic load simulation module includes: Real-time parameter acquisition unit and dynamic damping control unit; Real-time parameter acquisition unit: After the test system is initialized and a communication connection is established with the servo under test, it can acquire the current angular velocity data and current commanded rotation angle data of the servo under no-load or constant-load conditions in real time. The current angular velocity data and the current commanded rotation angle data are combined to generate the real-time operating parameters of the servo motor, and the real-time operating parameters are sent to the dynamic damping control unit. Dynamic damping control unit: used to receive the real-time operating parameters, perform model matching in a preset fluid resistance model library based on the real-time operating parameters, and extract the corresponding nonlinear damping coefficient; Based on the aforementioned nonlinear damping coefficient, a sudden, dynamically changing follow-up damping load is applied to the servo motor to simulate the dynamic electromechanical conditions of the servo motor in harsh industrial scenarios.

3. The servo motor performance parameter testing system according to claim 2, characterized in that, The micro-feature extraction module includes: Waveform instruction configuration unit and torque pulsation analysis unit; Waveform command configuration unit: used to acquire the preset sine wave test waveform command issued by the host computer software, and generate a trigger signal when the test waveform command runs to the peak or trough node with the maximum acceleration; The trigger signal is sent to the dynamic load simulation module, and the test waveform command is linked with the follow-up damping load to generate transient drag impact on the servo motor under test. Torque pulsation analysis unit: used to collect torque data inside the servo motor at a preset ultra-high frequency acquisition speed at the instant when the transient resistance impact occurs, to obtain real-time torque feedback data, and to plot the real-time torque feedback data into a high-frequency torque curve; The high-frequency torque curve is filtered to remove the basic mechanical torque portion, thus obtaining the high-frequency pulsating torque characteristics.

4. The servo motor performance parameter testing system according to claim 3, characterized in that, The concurrent crosstalk monitoring module includes: Concurrent instruction scheduling unit and bus waveform analysis unit; Concurrent command scheduling unit: used to send multi-channel concurrent commands to the servo under test on multiple channels simultaneously according to the user's multi-channel selection operation; This allows the tested servos in multiple test channels to operate simultaneously under the dynamic electromechanical conditions and synchronously perform emergency stop or instantaneous reversing actions under extreme conditions to generate the maximum transient current. Bus waveform analysis unit: used to collect voltage and current waveform data on the test bench system bus when the servo under test performs emergency stop or instantaneous reversing action on multiple channels; The voltage waveform data and the current waveform data are anomaly identified to obtain abnormal ripple characteristics. Based on the abnormal ripple characteristics, the bus ripple mutation data generated by the back electromotive force generated by the servo motor is recorded, and system-level crosstalk data is obtained from the bus ripple mutation data.

5. The servo motor performance parameter testing system according to claim 4, characterized in that, The thermal effect tracking module includes: Real-time temperature monitoring unit and zero-point drift calculation unit; Real-time temperature monitoring unit: used to monitor the real-time temperature of the casing and the internal motor of the servo under test after the servo has undergone long-term high-intensity concurrent operation in multiple channels. Determine whether the real-time temperature of the outer casing and / or the real-time temperature of the internal motor reaches or exceeds a preset heating temperature threshold. If it is determined that the heating temperature threshold has been reached or exceeded, the current dynamic test is interrupted and a zero-point query command is sent to the zero-point drift calculation unit. Zero-point drift calculation unit: After receiving the zero-point query command, it controls the tested servo to perform a return-to-zero action and collects the mechanical absolute zero-point data of the tested servo after returning to zero; The mechanical absolute zero position data is compared with the initial electrical zero position recorded by the tested servo motor in a cold state to obtain the physical deformation deviation data caused by heat accumulation. Zero point thermal drift data is generated based on the physical deformation deviation data.

6. The servo motor performance parameter testing system according to claim 5, characterized in that, The communication impairment injection module includes: Damage signal generation unit and runaway response extraction unit; Damage signal generation unit: used to randomly generate communication damage signals, including communication packet loss, message out-of-order and extreme command delay, within the bus protocol configuration layer during the operation of the tested servo motor under the dynamic electromechanical conditions. The communication impairment signal is continuously injected into the network data stream that is communicating with the servo under test, so that the underlying controller of the servo under test is in an extremely harsh state where the electromechanical environment and communication network are blocked. Runaway response extraction unit: used to monitor and extract the runaway protection response data of the underlying controller of the servo under test in real time when the servo under test is in the extreme severe state; The runaway protection response data specifically includes the precise delay time of the tested servo triggering the power-off protection mechanism, the torque maintenance data in the automatic braking lock-up state, and the system automatic reconnection time after network communication is restored.

7. The servo motor performance parameter testing system according to claim 6, characterized in that, The comprehensive evaluation and prediction module includes: Multidimensional feature fusion unit and health assessment unit; Multi-dimensional feature fusion unit: used to extract the torque pulsation features, system-level crosstalk data, zero-point thermal drift data and runaway protection response data obtained in the test process, and use them as the multi-dimensional state representation vector of the device; The multidimensional state representation vector is normalized and cross-dimensional deep feature fusion is performed to generate a standard input dataset. Health assessment unit: used to input the standard input dataset into a pre-set long short-term memory network for deduction, and construct a multi-dimensional health assessment model of the tested servo motor; Based on the multi-dimensional health assessment model, the health index and pass rate evaluation of the tested servo motor are output. Combining the health index and the pass rate evaluation, a quality consistency report is generated.

8. The servo motor performance parameter testing system according to claim 7, characterized in that, The comprehensive evaluation and prediction module also includes: Degradation early warning push unit; Degradation warning push unit: used to predict the life degradation of the core components of the tested servo motor based on the health index and the pass rate evaluation, and obtain the predicted life degradation information. Based on the predicted lifespan decay information, the remaining lifespan curve of the core components of the tested servo motor is constructed, and the decay trend of the remaining lifespan curve is extracted. By combining the remaining lifespan curve and the degradation trend, device degradation information of the tested servo motor is generated; Based on the equipment degradation information, the predicted remaining lifespan and failure type of the tested servo motor are obtained, and the severity level is classified according to the predicted remaining lifespan and failure type to obtain the target hazard level. Based on the target hazard level, an early warning message is generated and sent to maintenance personnel.

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