A motor coupling parameter collaborative analysis method and system based on micro-motion regulation
By performing micro-motion control on the motor and co-analyzing the operating parameters of the driven object, the limitations of existing motor fault early warning systems have been solved, enabling early, sensitive, and robust early warning of motor faults.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- STATE GRID SHANGHAI MUNICIPAL ELECTRIC POWER CO
- Filing Date
- 2025-10-31
- Publication Date
- 2026-07-31
AI Technical Summary
Existing motor fault early warning systems mainly rely on the analysis of motor body parameters, which has limitations, and the complex on-site environment makes it difficult to detect potential anomalies.
By manually controlling the motor to perform rapid acceleration or deceleration without affecting its normal operation, a controllable micro-motion operating condition segment is formed. Simultaneously, the motor's own parameters and the driving object's operating condition parameters are collected to establish a two-dimensional data mapping relationship, calculate the dynamic change rate index, and evaluate the consistency of the dynamic response between the motor and the load. If the change direction is inconsistent, an abnormality flag is triggered.
It improves the sensitivity and robustness of motor fault early warning, and can capture minute nonlinear mismatches when the overall operating data is normal, so as to realize the forward-looking early warning of potential early faults, breaking through the technical limitations of traditional reliance on a single source parameter.
Smart Images
Figure CN121500091B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of motor fault early warning technology, and more specifically to a method and system for collaborative analysis of motor coupling parameters based on micro-motion control. Background Technology
[0002] In industrial settings, electric motors are the primary power source, and their stable and reliable operation directly impacts production efficiency and safety. However, existing electric motor fault prediction systems mostly rely solely on analyzing the motor's intrinsic parameters, which has significant limitations. Moreover, the complex on-site environment often results in incomplete acquisition of motor parameters due to difficulties in sensor deployment, limited acquisition accuracy, and susceptibility to operating conditions, making it difficult to detect potential anomalies.
[0003] Therefore, how to more sensitively and accurately reflect the working status of motors and greatly improve the efficiency of motor fault detection and early warning is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention
[0004] In view of this, the present invention provides a method and system for collaborative analysis of motor coupling parameters based on micro-motion control, which solves the problems existing in the background technology.
[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for collaborative analysis of motor coupling parameters based on micro-motion control includes the following steps: Without affecting the normal operation of the motor, the motor can be manually controlled to perform rapid acceleration or deceleration within a preset time window, forming a controllable micro-motion operating condition segment. Simultaneously collect motor body parameters and motor drive object operating condition parameters to establish a two-dimensional data mapping relationship; Based on the preset time tags of the micro-motion working condition segment, the collected motor body parameters and motor drive object working condition parameters are classified by time. Based on multiple micro-action cycles, the dynamic change rate index of each physical quantity is calculated to form a cross-cycle comparable dynamic change rate matrix to evaluate the consistency of dynamic response between the motor and the load. Within the same micro-motion cycle, the change trend of the motor body parameters and the change trend of the driving object's operating condition parameters are curve-fitted and compared in the time domain. If the change direction is inconsistent, an anomaly flag is triggered.
[0006] Optional, motor body parameters include: motor input voltage. u Three-phase input current i Motor speed n .
[0007] Optional, the operating parameters of the motor-driven object include: water pump system pipeline pressure. Flow rate of water pump system pipeline Wind pressure of the fan system Flow rate of fan system pipeline .
[0008] Optional, the specific details of the micro-motion working condition segment are as follows: Rapid acceleration state: During rapid acceleration Inside, the motor speed is reduced from the normal operating speed. Increase to rapid acceleration speed ; Rapid acceleration to a stable state: when the motor speed reaches... After that, it ran stably for a period of time. ; Rapid deceleration state: During the rapid deceleration time Inside, the motor speed is reduced from a rapid acceleration speed. Reduce to normal operating speed ; Rapid deceleration stabilization state: when the motor speed reaches After that, it ran stably for a period of time. ; Once the motor finishes its micro-motion, it returns to its original normal operating speed without affecting the original device.
[0009] Optionally, the micro-motion amplitude can be controlled within ±5%.
[0010] Optionally, triggering the anomaly flag involves visualizing the rate of change trend curve and, in conjunction with the calculation results of the dynamic rate of change index, indicating the consistency of the trend through curve overlap and color marking.
[0011] Optionally, the method further includes: For different load characteristics, flexible control and scenario adaptation are achieved by adjusting micro-motion parameters; where the micro-motion parameters are... - time, and The speed range and load characteristics include differences in inertia and hysteresis.
[0012] This invention also discloses a motor coupling parameter collaborative analysis system based on micro-motion control, comprising: The motor control module is used to manually and actively control the motor to perform rapid acceleration or deceleration within a preset time window without affecting the normal operation of the motor, thus forming a controllable micro-motion operating condition segment. The data acquisition module is used to simultaneously collect the parameters of the motor body and the operating parameters of the motor driven object, and establish a two-dimensional data mapping relationship. The data classification module is used to classify the collected motor body parameters and motor drive object operating parameters according to time based on the preset time label of the micro-motion operating condition segment. The consistency identification module calculates the dynamic change rate index of each physical quantity based on multiple micro-action cycles, constructs a cross-cycle comparable dynamic change rate matrix, and evaluates the consistency of the dynamic response between the motor and the load. The visualization module is used to perform curve fitting and time-domain comparison of the change trends of the motor body parameters and the change trends of the driving object's operating parameters within the same micro-motion cycle. If the change directions are inconsistent, an anomaly marker is triggered.
[0013] Optional parameters for the motor itself include: motor input voltage, three-phase input current, and motor speed.
[0014] Optionally, the operating parameters of the motor drive object include: water pump system pipeline pressure, water pump system pipeline flow rate, fan system wind pressure, and fan system pipeline flow rate.
[0015] Optionally, the micro-motion amplitude can be controlled within ±5%.
[0016] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method and system for collaborative analysis of motor coupling parameters based on micro-motion control. By controlling the micro-acceleration and deceleration of the motor and simultaneously collecting the motor body parameters and the driving object operating condition parameters, it realizes deep coupling analysis of multi-source data, and exhibits beneficial effects in the following aspects: 1) Breaking the technical bottleneck of traditional isolated parameter analysis: This invention innovatively incorporates the operating parameters of the motor-driven object into the fault early warning analysis system. By actively constructing the motor acceleration and deceleration process, it makes up for the shortcomings of existing technologies that rely only on single parameters such as motor voltage, current, and speed, thereby effectively improving the sensitivity and robustness of the early warning. 2) Dynamic change rate and trend consistency analysis mechanism: This invention no longer relies on a simple comparison of absolute values, but constructs a mathematical index system through the change rate of multiple parameters in the acceleration and deceleration phases, and further performs trend consistency judgment (such as whether the curve direction is consistent), thereby capturing small nonlinear mismatches under the premise that the overall running data is not abnormal, and realizing a forward-looking early warning of potential early faults.
[0017] In summary, this invention, through multi-dimensional technological innovation, breaks through the limitations of traditional motor fault detection, which relies on a single source parameter, is passively monitored, and has a delayed response. It improves the accuracy, initiative, and intelligence of fault early warning and has broad application value and engineering promotion prospects. Attached Figure Description
[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.
[0019] Figure 1 A flowchart of the motor coupling parameter collaborative analysis method based on micro-motion control provided by the present invention; Figure 2 The rapid acceleration phase rate of change curve provided by this invention; Figure 3 The curve of the rate of change during the rapid deceleration phase is provided for the present invention. Detailed Implementation
[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0021] In the field of industrial automation, electric motors are the core power equipment. When operating at constant speed or constant torque, early faults (such as micro-wear of bearings) cannot be effectively identified through changes in the motor's intrinsic parameters. Although acceleration and deceleration processes under natural operating conditions contain fault characteristics, their duration and amplitude are random, making it difficult to systematically acquire effective transient data. Furthermore, the internal parameters of the motor are constrained by the field environment, making them prone to data loss or distortion. Therefore, there is an urgent need to develop a dynamic diagnostic method that can actively stimulate and accurately capture early fault symptoms to overcome these technical bottlenecks.
[0022] Therefore, this invention discloses a method for collaborative analysis of motor coupling parameters based on micro-motion control, such as... Figure 1 As shown, it includes the following steps: Without affecting the normal operation of the motor, the motor can be manually controlled to perform rapid acceleration or deceleration within a preset time window, forming a controllable micro-motion operating condition segment. Simultaneously collect motor body parameters and motor drive object operating condition parameters to establish a two-dimensional data mapping relationship; Based on the preset time tags of the micro-motion working condition segment, the collected motor body parameters and motor drive object working condition parameters are classified by time. Based on multiple micro-action cycles, the dynamic change rate index of each physical quantity is calculated to form a cross-cycle comparable dynamic change rate matrix to evaluate the consistency of dynamic response between the motor and the load. Within the same micro-motion cycle, the change trend of the motor body parameters and the change trend of the driving object's operating condition parameters are curve-fitted and compared in the time domain. If the change direction is inconsistent, an anomaly flag is triggered.
[0023] The core technical solution of the motor coupling parameter collaborative analysis method based on micro-motion control proposed in this embodiment lies in: by performing slight artificially controllable excitation on the motor (i.e., micro-motion control), combined with the coupled analysis of motor body data and driving object operating parameters, early warning of potential motor faults can be achieved. This solution adopts a technical path of "micro-motion control - coupled data acquisition - dynamic change rate analysis - trend consistency diagnosis," overcoming the problems of perception lag, data limitations, and diagnostic blind spots inherent in existing methods that rely on steady-state monitoring and single-source analysis of motor parameters. Specifically: (a) Data collection Analyzing and judging motor faults using motor parameters (such as motor voltage, motor current, motor speed, motor temperature, etc.) has limitations, including: inconvenience in collecting motor parameters, inaccuracy in collecting them, incomplete collection, and sometimes it is impossible to collect them at all due to the actual situation on site; it is also difficult to detect some potential motor faults using motor parameters.
[0024] Compared to the parameters of the motor itself, the parameters corresponding to the operating conditions of the motor driven object are easier to collect data on. For example, water pump motors (pipeline water pressure, pipeline water flow, etc.) and fan motors (wind pressure, temperature, wind speed, etc.) can have their motor faults deduced by analyzing the operating parameters of the motor driven object, thus enabling fault warnings for the drive motor.
[0025] Therefore, to overcome the technical bottleneck of traditional isolated parameter analysis, this embodiment simultaneously collects motor body parameters and motor drive object operating condition parameters for motor fault early warning. The motor body parameters include: motor input voltage. u Three-phase input current i Motor speed n The operating parameters of the motor-driven object include: water pump system pipeline pressure. Flow rate of water pump system pipeline Wind pressure of the fan system Flow rate of fan system pipeline .
[0026] (ii) Micro-motion control
[0027] To ensure stable operating conditions, motors typically operate at constant speed or torque, meaning the load is constant. Under these constant speed or torque conditions, it's difficult to detect early warning signs of motor malfunctions. Many abnormal motor faults occur during loading / unloading, acceleration / deceleration, and other processes. However, in actual engineering projects, these loading / unloading and acceleration / deceleration conditions are not very frequent, and their timing is highly random and uncertain, depending on the specific circumstances. Therefore, obtaining a large amount of accurate data on the motor's loading / unloading process in practical engineering is not easy.
[0028] Therefore, in this embodiment, without affecting the normal operation of the motor, the motor is manually controlled to slightly accelerate or decelerate, and the parameters of the motor itself and the operating conditions of the object driven by the motor are monitored. Based on the changes in these parameters during the slight acceleration or deceleration of the motor, early warning of motor faults can be achieved.
[0029] Specifically, the details of the micro-motion operating condition section are as follows: 1) Rapid acceleration state: During rapid acceleration... Inside, the motor speed is reduced from the normal operating speed. Increase to rapid acceleration speed ; 2) Rapid acceleration to a stable state: when the motor speed reaches... After that, it ran stably for a period of time. ; 3) Rapid deceleration state: During the rapid deceleration time Inside, the motor speed is reduced from a rapid acceleration speed. Reduce to normal operating speed ; 4) Rapid deceleration stabilization state: when the motor speed reaches... After that, it ran stably for a period of time. ; 5) Once the motor finishes its micro-motion, it returns to its original normal operating speed without affecting the original device.
[0030] Note: In order not to affect the normal operation of the original device, the amplitude of the micro-motion is generally controlled within ±5%.
[0031] (III) Data Classification
[0032] During the micro-movement of the motor, based on the rapid acceleration time Rapidly accelerate stable operation time Rapid deceleration time Rapid deceleration stable operation time It accurately classifies the motor acquisition data and the motor drive object acquisition data according to time, completely avoiding the previous situation of unclear motor operating conditions.
[0033] (iv) Fault Analysis
[0034] To enable early warning analysis of motor faults, and to address the issue that simply measuring and analyzing motor data is insufficient for fault analysis, this embodiment compares and analyzes the data collected from the motor drive object within the same operating condition period with the motor data itself. Specifically: 1) Rapid acceleration state: The motor speed starts at... Water pump system pipeline pressure Water pump system pipeline flow rate Wind pressure of the fan system Flow rate in the fan system pipeline During the rapid acceleration of the motor It then enters a state of rapid acceleration and stabilization; 2) Rapid acceleration to stable state: During the rapid acceleration of the motor Afterwards, it enters a rapid acceleration and stabilization state, with the motor speed accelerating to [value missing]. When the motor speed reaches Afterwards, the water pump system pipeline pressure of the motor-driven object Water pump system pipeline flow rate Wind pressure of the fan system Flow rate in the fan system pipeline ; 3) Rapid deceleration state: The motor speed starts at... Water pump system pipeline pressure Water pump system pipeline flow rate Wind pressure of the fan system Flow rate in the fan system pipeline During the rapid deceleration of the motor It then enters a state of rapid deceleration and stabilization; 4) Rapid deceleration stabilization state: During the rapid deceleration time of the motor Afterwards, it enters a rapid deceleration and stabilization state, and the motor speed decreases to [value missing]. Afterwards, the water pump system pipeline pressure of the motor-driven object Water pump system pipeline flow rate Wind pressure of the fan system Flow rate in the fan system pipeline .
[0035] Without affecting the normal operation of the motor and the motor-driven object, repeat the above steps of micro-motion control, data classification, and fault analysis. That is, manually and actively control the motor to slightly accelerate or decelerate, and accurately analyze and organize the motor-collected data and the motor-driven object-collected data based on the motor micro-motion time to obtain the motor-driven object data shown in Table 1.
[0036] Table 1 Data on Motor Driven Objects
[0037] However, traditional methods of simply comparing data sizes are insufficient to detect data anomalies. Therefore, this embodiment compares the dynamic change rate of the motor drive object data in the motor acquisition dataset, which more accurately reflects the motor's operating status, as shown in Table 2.
[0038] Table 2 Analysis of Dynamic Change Rate
[0039] By comparing the curves of the motor parameters and the parameters of the motor-driven object collected within the same time period, it can be found that the overall trend of the rate of change is consistent (both are increasing or both are decreasing). If the trends are inconsistent, it indicates that the motor or the motor-driven object is not in good condition, thus providing an early warning for motor failure.
[0040] in conclusion: Figure 2 The diagram shows the rate of change curves for various parameters during the rapid acceleration phase. Under normal circumstances, the trends of the acceleration rate of change curve, the pressure rate of change curve, and the flow rate rate of change curve should be the same, but... Figure 2 During the period when both the acceleration rate change curve and the flow rate change curve rise simultaneously, the pressure rate change curve declines, indicating a motor malfunction. Figure 3 The diagram shows the rate of change curves for various parameters during the rapid deceleration phase. Under normal circumstances, the trends of the deceleration rate of change curve, the deceleration pressure rate of change curve, and the deceleration flow rate rate of change curve are the same, but... Figure 3 During the period when both the deceleration rate curve and the depressurization rate curve are decreasing, the depressurization flow rate curve is increasing, indicating that the motor has malfunctioned.
[0041] Furthermore, the specific method for triggering the anomaly marker is to visualize the trend curve of the rate of change and, together with the calculation results of the dynamic rate of change index, indicate the consistency of the trend by means of curve overlap and color marking.
[0042] Furthermore, the technical solution in this embodiment also includes: For different load characteristics, flexible control and scenario adaptation are achieved by adjusting micro-motion parameters; where the micro-motion parameters are... - time, and The speed range and load characteristics include differences in inertia and hysteresis.
[0043] and Figure 1Corresponding to the method described above, this embodiment of the invention also provides a motor coupling parameter collaborative analysis system based on micro-motion control, used for... Figure 1 The specific implementation of the method, as provided in this embodiment of the invention, is a motor coupling parameter collaborative analysis system based on micro-motion control, which can be applied to computer terminals or various mobile devices, specifically including: The motor control module is used to manually and actively control the motor to perform rapid acceleration or deceleration operations within a preset time window without affecting the normal operation of the motor, forming a controllable micro-motion operating condition segment. In this embodiment, the system uses the built-in motor control module to apply rapid acceleration and deceleration operations within a range of ±5% to the motor through small-amplitude, short-cycle active adjustment methods, forming a controllable micro-motion operating condition segment. During this process, it ensures that the main task of the motor continues to operate without being affected, and the adjustment process has the characteristics of repeatability and clear time window, which facilitates data archiving and comparative analysis. The data acquisition module is used to simultaneously collect the parameters of the motor body and the operating parameters of the motor driven object, and establish a two-dimensional data mapping relationship. The data classification module is used to classify the collected motor body parameters and motor drive object operating parameters according to time based on the preset time label of the micro-motion operating condition segment. The consistency identification module, based on multiple micro-action cycles, calculates the dynamic change rate index of each physical quantity, constructs a cross-cycle comparable dynamic change rate matrix, and evaluates the consistency of the dynamic response between the motor and the load; specifically, it calculates the motor acceleration change rate for the acceleration and deceleration phase data within each micro-action cycle. Rate of change of applied pressure , Reduced flow rate etc., to explore the nonlinear changing trends in the system operation; The visualization module is used to perform curve fitting and time-domain comparison of the change trends of the motor body parameters and the change trends of the driving object's operating parameters within the same micro-motion cycle. If the change directions are inconsistent, an anomaly marker is triggered.
[0044] Furthermore, the motor's parameters include: motor input voltage, three-phase input current, and motor speed.
[0045] Furthermore, the operating parameters of the motor-driven object include: water pump system pipeline pressure, water pump system pipeline flow rate, fan system wind pressure, and fan system pipeline flow rate.
[0046] Furthermore, the range of micro-movements is controlled within ±5%.
[0047] The proposed motor coupling parameter collaborative analysis system based on micro-motion control in this embodiment can construct a trend consistency evaluation model under multiple micro-motion sampling, improving the sensitivity to minor fault symptoms and avoiding misjudgments caused by isolated point disturbances. It supports batch operation of the micro-motion testing process, with each round of acceleration / deceleration corresponding to a set of independent sampling data and rate of change records, allowing for periodic micro-motion detection without interrupting production tasks. This system is highly versatile and applicable to typical motor load systems such as water pumps and fans. For different load characteristics (such as differences in inertia and hysteresis), the system can adjust micro-motion parameters (e.g., ... - time, and The system enables flexible control and scenario adaptation within a specific speed range. It visualizes the trend curve and, in conjunction with dynamic rate of change calculations, uses curve overlap and color coding to indicate trend consistency, providing clear and intuitive diagnostic support for on-site maintenance personnel. Without relying on high-cost sensor deployment, it effectively identifies early fault symptoms based on the collaborative response characteristics of multi-source data induced by motor micro-motions, improving the safety, predictability, and intelligence of motor operation.
[0048] In summary, addressing the bottlenecks of existing technologies, this embodiment proposes a method and system for collaborative analysis of motor coupling parameters based on micro-motion control. Its innovation lies in: 1) Micro-move proactive incentive mechanism Through controlled micro-speed adjustment (±5%) Generate standardized transient conditions: within a preset time window ( / Precisely trigger rapid acceleration within ( ) → ), rapid deceleration ( → ), and maintain a steady state ( / This ensures that the excitation process does not affect the normal operation of the equipment, while constructing a repeatable fault characteristic excitation environment.
[0049] 2) Driving object-ontology parameter co-validation model
[0050] The system synchronously collects motor body data and driving object operating condition parameters to establish a two-dimensional data mapping relationship. By comparing the rate of change of motor speed and the rate of change of driving object parameters within the same time period, it identifies response consistency. If the trend of speed change deviates from the trend of pressure and flow ratio change, a motor fault warning is triggered.
[0051] 3) Time-series correlation analysis of dynamic rate of change
[0052] Based on multiple micro-action cycles, key transient indicators (such as acceleration change rate, pressure change rate, and flow change rate) are extracted, and a change rate matrix is constructed. By comparing historical data longitudinally and cross-validating laterally, early fault characteristics are amplified.
[0053] 4) Fault-Sensitivity Enhanced Data Classification
[0054] Based on preset time tags ( / / / Automatically segment the data stream to eliminate the risk of misjudging operating conditions and focus on transient-steady-state transition nodes (such as the end time of rapid acceleration t= This helps to lock onto abnormal modes such as hysteresis response and overshoot oscillation of the driving object parameters, thereby improving the detection rate of hidden faults.
[0055] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.
[0056] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for motor coupling parameter collaborative analysis based on micro-motion regulation, characterized in that, Includes the following steps: Without affecting the normal operation of the motor, the motor can be manually controlled to perform rapid acceleration or deceleration within a preset time window to form a controllable micro-motion operating condition segment; the parameters of the motor body and the operating condition parameters of the motor driven object are collected simultaneously to establish a two-dimensional data mapping relationship. Based on the preset time tags of the micro-motion working condition segment, the collected motor body parameters and motor drive object working condition parameters are classified by time. Based on multiple micro-action cycles, the dynamic change rate index of each physical quantity is calculated to form a cross-cycle comparable dynamic change rate matrix to evaluate the consistency of dynamic response between the motor and the load. Within the same micro-action cycle, the change trend of the motor body parameters and the change trend of the driving object's operating condition parameters are curve-fitted and compared in the time domain. If the change direction is inconsistent, an anomaly flag is triggered.
2. The method for collaborative analysis of motor coupling parameters based on micro-motion control according to claim 1, characterized in that, The motor body parameters include: motor input voltage u , three-phase input current i , and motor speed n .
3. The method for collaborative analysis of motor coupling parameters based on micro-motion control according to claim 1, characterized in that, Operating parameters of the motor-driven object include: water pump system pipeline pressure. Flow rate of water pump system pipeline Wind pressure of the fan system and fan system pipeline flow .
4. The method for collaborative analysis of motor coupling parameters based on micro-motion control according to claim 1, characterized in that, The specific details of the micro-motion operating condition section are as follows: Rapid acceleration state: During rapid acceleration Inside, the motor speed is reduced from the normal operating speed. Increase to rapid acceleration speed ; Rapid acceleration to a stable state: when the motor speed reaches... After that, it ran stably for a period of time. ; Rapid deceleration state: During the rapid deceleration time Inside, the motor speed is reduced from a rapid acceleration speed. Reduce to normal operating speed ; Rapid deceleration stabilization state: when the motor speed reaches After that, it ran stably for a period of time. ; Once the motor finishes its micro-motion, it returns to its original normal operating speed without affecting the original device.
5. The method for collaborative analysis of motor coupling parameters based on micro-motion control according to claim 1, characterized in that, The range of micro-movements should be controlled within ±5%.
6. The method for collaborative analysis of motor coupling parameters based on micro-motion control according to claim 1, characterized in that, The specific method for triggering anomaly markers is to visualize the change rate trend curve and, together with the calculation results of the dynamic change rate index, indicate the trend consistency status by using curve overlap and color marking.
7. The method for collaborative analysis of motor coupling parameters based on micro-motion control according to claim 4, characterized in that, Also includes: For different load characteristics, flexible control and scenario adaptation are achieved by adjusting micro-motion parameters; where the micro-motion parameters are... - time, and The speed range, load characteristics including differences in inertia and hysteresis.
8. A motor coupling parameter collaborative analysis system based on micro-motion control, characterized in that, include: The motor control module is used to manually and actively control the motor to perform rapid acceleration or deceleration within a preset time window without affecting the normal operation of the motor, forming a controllable micro-motion operating condition segment; the data acquisition module is used to synchronously collect the motor body parameters and the operating condition parameters of the motor driven object, and establish a two-dimensional data mapping relationship; the data classification module is used to classify the collected motor body parameters and the operating condition parameters of the motor driven object according to time based on the preset time label of the micro-motion operating condition segment; the consistency identification module calculates the dynamic change rate index of each physical quantity based on multiple micro-motion cycles, constructs a cross-cycle comparable dynamic change rate matrix, and evaluates the consistency of the dynamic response between the motor and the load; The visualization module is used to perform curve fitting and time-domain comparison of the change trends of the motor body parameters and the change trends of the driving object's operating parameters within the same micro-motion cycle. If the change directions are inconsistent, an anomaly marker is triggered.
9. The motor coupling parameter collaborative analysis system based on micro-motion control according to claim 8, characterized in that, The parameters of the motor itself include: motor input voltage, three-phase input current and motor speed.
10. A motor coupling parameter collaborative analysis system based on micro-motion control according to claim 8, characterized in that, The operating parameters of the motor-driven object include: water pump system pipeline pressure, water pump system pipeline flow rate, fan system wind pressure, and fan system pipeline flow rate.
11. The motor coupling parameter collaborative analysis system based on micro-motion control according to claim 8, characterized in that, The range of micro-movements should be controlled within ±5%.