Intelligent electrical equipment maintenance task decision support system and method

Abstract

The present invention discloses an intelligent electrical equipment maintenance task decision support system and method, which specifically relates to the field of motor maintenance and is used to solve the problem of running-in mismatch between new and old components. The system collects multi-dimensional performance parameters of new and old components, builds a performance feature database, and describes the differences in key parameters in detail. A support vector machine regression algorithm is used to generate a personalized running-in curve, and the load change rate, speed adjustment step size, and running-in time are accurately set to avoid vibration and current fluctuations caused by initial mismatch between new and old components. The system automatically adjusts the load and speed to ensure a smooth running-in process by real-time monitoring of the equipment's temperature, vibration spectrum, and current fluctuations. The system classifies and analyzes the running-in status in combination with the spectrum sharpness index and the time series entropy difference index to promptly identify potential anomalies. In an ambiguous state, the system optimizes the running-in by adjusting the PID parameters to ensure that the new and old components gradually match, improve the equipment's operating stability, and avoid failures and damage caused by long-term poor running-in.

Description

Technical Field

[0001] The present invention relates to the field of motor maintenance, and more particularly to an intelligent electrical equipment maintenance task decision support system and method. Background Art

[0002] With the widespread use of electrical equipment in various industrial production and infrastructure, the stable operation of the equipment has a significant impact on the reliability of the overall system. In the fields of power, manufacturing, and transportation, a large number of electrical equipment need to operate continuously for long periods of time. Their key components will inevitably wear, age, and degrade in performance after experiencing high loads and frequent starts and stops. To ensure the normal operation of the equipment and extend its service life, regular maintenance, inspections, and troubleshooting have become the core links in equipment management. With the increasing complexity of electrical equipment, maintenance work is not limited to traditional fault repair, but also covers preventive maintenance and predictive maintenance. Through precise detection and component replacement, the impact of sudden failures on the entire system can be avoided.

[0003] As the core device in electrical equipment, the stable operation of the motor is crucial to the reliability of the entire system. However, after long-term use, the aging of motor components and performance degradation are particularly prominent. Traditional maintenance methods are usually fault-repair-oriented, restoring equipment operation in the short term by replacing aging or damaged components in the motor. However, existing maintenance technologies often overlook the running-in problem between new and old components. Due to the improved performance of new components, they may initially have performance differences with other older components in the motor, especially in key parameters such as speed, torque, and electrical impedance. The new and old components are not fully matched and run-in, resulting in increased vibration and current fluctuations during equipment operation, and even aggravated wear of mechanical components.

[0004] This imbalance between new and old components not only affects the motor's short-term performance but can also lead to the gradual accumulation of operational hazards. Existing technologies lack real-time monitoring and parameter adjustment during the running-in period between new and old components, impacting the stability of the equipment after maintenance and negatively affecting its long-term reliability.

[0005] In order to solve the above problems, a technical solution is now provided. Summary of the Invention

[0006] To overcome the aforementioned shortcomings of the prior art, embodiments of the present invention provide an intelligent electrical equipment maintenance task decision support system and method, offering an effective solution for the run-in process of new and old motor components. First, by collecting multi-dimensional performance parameters of new and old components and constructing a performance characteristic database, the system can detail the key parameter differences between components, particularly factors that influence the run-in effect, such as speed, torque, and electrical impedance. Second, a support vector machine regression algorithm is used to generate a personalized run-in curve. The load change rate, speed adjustment step size, and run-in time are precisely set based on actual operating conditions, thus avoiding vibration and current fluctuations caused by initial mismatches between the new and old components. Furthermore, the system monitors the device's temperature, vibration spectrum, and current fluctuations in real time. If any parameter exceeds a threshold during the run-in process, the adaptive control algorithm automatically adjusts the load and speed, ensuring a smooth and orderly run-in process. By extracting frequency and time domain features, calculating the spectral sharpness index and time series entropy difference index, and combining them with the time-frequency gain fusion index, the system classifies and analyzes the run-in status. This system effectively identifies potential anomalies after the replacement of new components and displays abnormal, ambiguous, or normal maintenance status. Especially in ambiguous situations, by adjusting the PID parameters of the equipment controller, the running-in process of new and old components is further optimized, ensuring that the performance of new and old components gradually matches, avoiding long-term performance degradation and equipment stability issues caused by insufficient running-in. This technical solution not only enables real-time monitoring and dynamic adjustment of the running-in period, solving the problem of neglecting the running-in period of new and old components in the existing technology, but also improves the operating stability and long-term reliability of the motor equipment by optimizing the running-in process, preventing secondary failures and mechanical damage caused by improper running-in, and thus solving the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] S1, collects performance parameters of new and old components and builds a multi-dimensional performance characteristics database;

[0009] S2 uses the support vector machine regression algorithm to process multi-dimensional performance characteristic data, generate a personalized running-in curve, and set the load change rate, speed adjustment step size, and operating time during the running-in period;

[0010] S3 monitors the device's temperature, vibration spectrum, and current fluctuations in real time, and uses control algorithms to dynamically adjust load and speed when parameters exceed thresholds.

[0011] S4, based on the frequency domain and time domain feature extraction of the vibration signal during the running-in process of the old and new parts, calculates the spectrum sharpness index and time series entropy difference index, and uses the time-frequency gain fusion index to identify potential running-in anomalies after the replacement of new equipment parts, and presents the maintenance abnormality state, ambiguous state or normal maintenance state;

[0012] S5, if a maintenance ambiguous state is obtained, the PID parameters of the equipment controller are adjusted to optimize the operating parameters during the running-in process.

[0013] In a preferred embodiment, step S2 includes the following:

[0014] First, the multi-dimensional performance data collected in step S1 is standardized;

[0015] The standardized multi-dimensional performance data is used as the training dataset, the target output value is the running-in state of the equipment, and the mean square error is used as the loss function for model training to optimize the parameters and penalty factors of the SVR model.

[0016] After training is completed, the SVR model predicts the running-in status under different working conditions based on the characteristic data of new and old components; based on the predicted running-in status, it generates the corresponding running-in curve, which reflects the relationship between load change, speed adjustment and running time.

[0017] In a preferred embodiment, step S3 includes the following contents:

[0018] A set of threshold ranges are preset based on the operating characteristics of the equipment. These thresholds include the upper temperature limit, the upper vibration frequency limit, and a safe range for current fluctuations. Once any parameter exceeds the preset threshold, the adaptive control logic is activated, dynamically adjusting the load and speed based on real-time data. The adjustment strategy is executed based on the type and degree of the parameter exceeding the threshold.

[0019] In a preferred embodiment, step S4 includes the following contents:

[0020] During motor operation, mechanical vibration data is collected in real time, and the vibration waveforms of the equipment under different load conditions are recorded. These signals are stored as time-domain data, representing the changes in the vibration intensity of the equipment over time. The vibration signal is assumed to be x(t), where t is time and x is the amplitude of the vibration. After each load adjustment, the vibration signal must be recollected to ensure data continuity. After the old component is replaced, the frequency sharpness index and time series entropy difference index are obtained based on the vibration signal generated by the new component after installation.

[0021] In a preferred embodiment, the logic for obtaining the spectrum sharpness index is:

[0022] First, the vibration signal x(t) in the time domain is converted to the frequency domain to obtain the spectral components of the vibration signal. For the vibration signal, the time domain signal x(t) is defined as: Among them, A is the amplitude of the main vibration signal, f0 is the main frequency, A n is the amplitude of other minor frequencies, f nare the secondary frequencies, φ and φ n is the phase angle;

[0023] The spectrum components are obtained by frequency domain transformation, and the spectrum is S(f), where f is the frequency and S(f) is the vibration intensity at frequency f;

[0024] The distribution characteristics of the spectrum are quantified and the frequency distribution function F(f) is constructed to reflect the relative distribution of each frequency component; it is defined as follows: Among them, f max is the highest frequency range of the spectrum, |S(f)| 2 is the energy intensity at frequency f; normalized to ensure that the total energy is distributed between 0 and f max between;

[0025] Calculate the spectrum sharpness increment function to reflect the sudden change of local frequency energy in the spectrum; the local energy change rate based on frequency is expressed as follows: Where Δf is the frequency interval, which represents the rate of change of local energy within the frequency range;

[0026] Taking all the frequency increments into account, the spectrum sharpness index SPI is calculated. By integrating the frequency increments and taking the curvature of the local increments into account as a correction term, we get: in, It is the second-order derivative at frequency f, reflecting the change in curvature of the frequency energy distribution.

[0027] In a preferred embodiment, the logic for obtaining the time series entropy difference index is:

[0028] Convert the continuous vibration signal x(t) into a discrete symbol sequence;

[0029] The amplitude of the vibration signal is divided into M intervals according to a predetermined threshold value and mapped to a symbol set {s1, s2, ..., s M}:s k =Φ(x(t i ))=s k , when τ k-1 <x(t i )≤τ k ; where τ0, τ1,…, τ M is the partition threshold, satisfying τ0<τ1<…<τ M ,k=1,2,…,M;

[0030] According to the statistical characteristics of the vibration signal, the threshold τ is set k :τ k =τ0+k·Δτ; where, Δτ=(τ M-τ0) / M, ensuring that the symbol intervals are of equal width or adjusted according to signal characteristics;

[0031] Using symbol sequences, the transition probability between symbols is calculated to reflect the dynamic changes of time series;

[0032] Calculation symbol s i Transfer to symbol s j Number of times Where δ is the indicator function, which takes 1 when the condition is met and 0 otherwise, and T is the length of the symbol sequence;

[0033] Calculate the transition probability matrix Ensure that the transition probability of each symbol satisfies the normalization condition:

[0034] Through the transfer probability matrix, the entropy difference between the current vibration signal and the normal state signal is calculated to obtain the time series entropy difference index; the current transfer entropy is calculated

[0035] Using the vibration signal under normal operating conditions, the corresponding transfer entropy H is calculated using the same method. normal ; Calculate the time series entropy difference index TSEDI: TSEDI = |HH normal |.

[0036] In a preferred embodiment, step S4 includes the following contents:

[0037] Two gain functions are calculated for the spectrum sharpness index and the time series entropy difference index, respectively, to express their sensitivity to potential anomalies in different dimensions: Combine the time-frequency gain functions into a time-frequency gain fusion index

[0038] The time-frequency gain fusion index is compared with the upper and lower thresholds. If the time-frequency gain fusion index is greater than or equal to the upper threshold, it indicates that there is an obvious abnormality in the running-in process, indicating that the newly replaced component is obviously not suitable for the current usage status of the equipment, and a maintenance abnormality status is displayed. If the time-frequency gain fusion index is greater than or equal to the lower threshold and less than the upper threshold, further analysis of the running-in situation is required, and adjustment of operating parameters to ensure safety is required, and a maintenance ambiguous status is displayed. If the time-frequency gain fusion index is less than the lower threshold, it indicates that the running-in process of the new and old components is stable, and a maintenance normal status is displayed.

[0039] In a preferred embodiment, step S5 includes the following contents:

[0040] Obtain the proportional, integral, and differential parameters of the current controller, monitor the speed, torque, and vibration signal amplitude of the equipment in real time, and analyze the response curve during load changes as a basis for subsequent PID parameter adjustments;

[0041] To address response speed issues during equipment run-in, the vibration signal's time-frequency gain fusion index is adjusted in real time. This adjustment is done by multiplying the original proportional coefficient by a factor related to the time-frequency gain fusion index. The size of the factor depends on the index's position between the upper and lower thresholds.

[0042] In view of the phenomenon of equipment error accumulation, the integral coefficient is adjusted to eliminate the steady-state error. The adjustment method is to make an additive adjustment to the original integral coefficient based on the total error accumulated during the operation of the equipment.

[0043] In response to sudden vibrations or fluctuations of the equipment in the short term, the instantaneous overshoot is suppressed by adjusting the differential coefficient. The adjustment method is to adjust the differential coefficient according to the instantaneous change rate of the time-frequency gain fusion index.

[0044] Intelligent electrical equipment maintenance task decision support system, including: performance acquisition module, algorithm processing module, monitoring and adjustment module, signal analysis module and parameter optimization module;

[0045] Performance acquisition module: collects the performance parameters of new and old components, builds a multi-dimensional performance characteristic database, outputs detailed performance characteristic data of new and old components, and passes it to the algorithm processing module;

[0046] Algorithm processing module: Based on the collected multi-dimensional performance characteristic data, it uses the support vector machine regression algorithm to generate a personalized running-in curve, set the load change rate, speed adjustment step size and running time during the running-in period, output the running-in curve and load and speed adjustment plan, and pass it to the monitoring and adjustment module;

[0047] Monitoring and Adjustment Module: This module monitors the device's temperature, vibration spectrum, and current fluctuations in real time. It uses control algorithms to dynamically adjust the load and speed when these parameters exceed preset thresholds. It then outputs the device's real-time monitoring data and adjusted operating status to the signal analysis module.

[0048] Signal analysis module: Based on the vibration signals in the monitoring data, it extracts frequency and time domain features, calculates the spectrum sharpness index and time series entropy difference index, and combines the time-frequency gain fusion index to identify abnormal equipment running-in status. It then outputs the abnormality classification and passes it to the parameter optimization module.

[0049] Parameter Optimization Module: If an ambiguous state is obtained, the PID parameters of the equipment controller are adjusted to optimize the operating parameters during the running-in process, and the optimized PID parameter settings are output to ensure that the performance of the new and old components gradually matches.

[0050] The technical effects and advantages of the intelligent electrical equipment maintenance task decision support system and method of the present invention are as follows:

[0051] The present invention proposes an effective solution for the running-in process of new and old parts of the motor. First, by collecting multi-dimensional performance parameters of new and old parts and constructing a performance characteristic database, the key parameter differences of different parts can be described in detail, especially the factors affecting the running-in effect such as speed, torque and electrical impedance. Secondly, a personalized running-in curve is generated using a support vector machine regression algorithm, and the load change rate, speed adjustment step and running-in time are accurately set according to the actual operating conditions, avoiding the vibration and current fluctuation problems caused by the initial mismatch between the new and old parts. In addition, the temperature, vibration spectrum and current fluctuation of the equipment are monitored in real time to ensure that when any parameter exceeds the threshold during the running-in process, the adaptive control algorithm can automatically adjust the load and speed to ensure a smooth and orderly running-in process. By extracting frequency domain and time domain features, calculating the spectrum sharpness index and time series entropy difference index, and combining the time-frequency gain fusion index, the running-in status is classified and analyzed, which can effectively identify potential abnormalities of the equipment after the new parts are replaced, and present abnormal, ambiguous or normal maintenance status. Especially in ambiguous situations, by adjusting the PID parameters of the equipment controller, the running-in process of new and old components is further optimized, ensuring that the performance of new and old components gradually matches, avoiding long-term performance degradation and equipment stability issues caused by insufficient running-in. This technical solution not only enables real-time monitoring and dynamic adjustment of the running-in period, resolving the shortcomings of existing technologies that ignore the running-in period between new and old components, but also improves the operating stability and long-term reliability of motor equipment by optimizing the running-in process, preventing secondary failures and mechanical damage caused by improper running-in. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a flow chart of the intelligent electrical equipment maintenance task decision support method of the present invention.

[0053] Figure 2 This is a flow chart of the intelligent electrical equipment maintenance task decision support system of the present invention. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0055] Example 1: Figure 1 The present invention provides an intelligent electrical equipment maintenance task decision support method, comprising:

[0056] S1, collects performance parameters of new and old components and builds a multi-dimensional performance characteristics database;

[0057] S2 uses the support vector machine regression algorithm to process multi-dimensional performance characteristic data, generate a personalized running-in curve, and set the load change rate, speed adjustment step size, and operating time during the running-in period;

[0058] S3 monitors the device's temperature, vibration spectrum, and current fluctuations in real time, and uses control algorithms to dynamically adjust load and speed when parameters exceed thresholds.

[0059] S4, based on the frequency domain and time domain feature extraction of the vibration signal during the running-in process of the old and new parts, calculates the spectrum sharpness index and time series entropy difference index, and uses the time-frequency gain fusion index to identify potential running-in anomalies after the replacement of new equipment parts, and presents the maintenance abnormality state, ambiguous state or normal maintenance state;

[0060] S5, if a maintenance ambiguous state is obtained, the PID parameters of the equipment controller are adjusted to optimize the operating parameters during the running-in process.

[0061] After long-term operation, key motor components such as the rotor, stator, and bearings are prone to wear and aging. To maintain proper motor operation, these components are typically replaced. However, performance differences between old and new components can lead to increased vibration and unbalanced mechanical loads after replacement, impacting the motor's overall operational stability. Therefore, after component replacement, it is essential to perform a run-in period for both the old and new components. To achieve this precise run-in management, it is first necessary to collect key performance parameters for both the new and old components and construct a data model encompassing multidimensional performance characteristics, which serves as the basis for subsequent run-in control and adjustments.

[0062] Step S1 includes the following contents:

[0063] Strain gauges, accelerometers, temperature sensors, vibration sensors, and current sensors are installed on key motor components. Strain gauges are primarily installed on the motor bearings and rotor to measure torque and mechanical strain. Accelerometers are used to monitor mechanical vibration and are installed on the motor housing and its mountings. Temperature sensors should be placed around the stator windings, rotor, and bearings to record temperature changes in each component. Current sensors are placed at the motor power input to monitor motor current fluctuations in real time. Vibration sensors are used to monitor the overall vibration spectrum and abnormal vibration behavior of the motor.

[0064] Select appropriate acquisition hardware, including a multi-channel data acquisition card and signal conditioning equipment. The acquisition card must have high-speed sampling capabilities (at least 10kHz) to capture dynamic performance changes during high-speed motor operation. Also, select a signal conditioning module that supports analog-to-digital conversion. This converts the analog signals collected by each sensor into processable digital signals for subsequent data processing and storage.

[0065] Before starting the motor, ensure all sensors are installed and connected to the data acquisition system. Start the motor and gradually apply different operating conditions, including no-load, half-load, and full-load conditions. Continuously collect performance parameters of both new and old components under different loads. Collected parameters include, but are not limited to, speed, torque, vibration frequency, temperature changes, and current fluctuations. During the collection process, ensure that each state runs for a sufficient period of time to ensure that sufficiently stable and representative parameter data is collected.

[0066] The collected performance data is transmitted to a central database in real time. The collected data is preprocessed, including data cleaning and outlier removal. Filtering techniques (such as Kalman filtering) are used to remove noise signals and retain valid performance parameters.

[0067] The processed performance data is categorized according to different dimensions to form a multidimensional database of performance characteristics for both new and existing components. Dimensions include mechanical characteristics (such as vibration frequency and torque), electrical characteristics (such as current fluctuation and impedance), thermal characteristics (such as temperature distribution), and operating characteristics under various load conditions. These multidimensional characteristics are stored using database technologies (such as MySQL or MongoDB) for easy access and analysis.

[0068] During motor operation, after component replacement, the performance differences between old and new components will affect the overall operation of the motor, especially key parameters such as torque, vibration frequency, and speed may not perform the same under different loads. In order to ensure that the motor can smoothly pass the running-in period of new and old components, it is crucial to generate a personalized running-in curve. By processing multi-dimensional performance characteristic data, the running-in process of new and old components can be predicted, and reasonable load change rates, speed adjustment steps, and running-in times can be provided for different operating conditions. The key to this step is to use the support vector machine regression algorithm (SVR) to process the collected multi-dimensional performance data to generate a running-in curve that adapts to the performance differences between new and old components.

[0069] Step S2 includes the following contents:

[0070] First, the multi-dimensional performance data collected in step S1 is standardized to ensure that data of different dimensions have the same dimension and avoid calculation deviation caused by dimensional differences.

[0071] Standardized multidimensional performance data is used as the training dataset, where each data point contains performance parameters such as torque, vibration frequency, and temperature under different load and speed conditions. The target output values ​​are the equipment's running-in status (such as vibration intensity, mechanical stress, and temperature rise rate), which are used to determine the degree of running-in. The mean squared error (MSE) is used as the loss function for model training, optimizing the parameters and penalty factors of the SVR model to ensure good generalization of the model's predictions about the running-in process.

[0072] After training is completed, the SVR model predicts the running-in status under different working conditions based on the characteristic data of new and old components. Through the predicted running-in status, the corresponding running-in curve is generated. The curve reflects the relationship between load change, speed adjustment and running time. The specific generation method is: for different load conditions, the SVR model outputs the corresponding running-in status values ​​(vibration, temperature rise, stress). According to these outputs, the load change rate and speed adjustment step size are set to ensure that the running-in process proceeds smoothly under different load conditions. For example, if the model predicts that the vibration is too large under a certain load, the load change rate under the load is set to a smaller value, and the speed step size is adjusted to reduce the impact of vibration on the components.

[0073] In the generated running-in curve, the load change rate and speed adjustment step size under each operating condition are optimized based on the performance prediction results. For example, the calculation formula for the load change rate ΔP is:

[0074] Among them, P final and P initial are the final load and initial load, respectively, and T is the preset running-in period. The speed adjustment step Δn is calculated based on the vibration frequency change predicted by the model and is calculated using the following formula:

[0075] Among them, n final and n initial These are the final and initial speeds respectively. Through this step size, the speed is gradually adjusted to make the running-in process of new and old parts smoother.

[0076] The running-in time is determined based on the running-in state change curve of the equipment. The running-in time predicted by the SVR model is corrected in combination with historical data to ensure that the running-in time under each load condition is long enough to achieve a good match between the components. The running-in time calculation formula is:

[0077] Where error is the difference between the current running-in state and the ideal state, α and β are constants adjusted based on historical data, and K is the maximum running-in time. This formula allows for dynamic adjustment of the running-in time under varying load conditions to ensure adequate running-in of components.

[0078] A personalized running-in curve is generated based on the performance data of new and old components and the SVR model, setting the load change rate, speed adjustment step size and running time to provide precise control parameters for the subsequent running-in process.

[0079] During the run-in process of new and old motor components, performance differences between components, as operating conditions such as equipment load and speed change, can lead to temperature rise, increased vibration, or current fluctuations. If these parameters exceed predetermined safety thresholds, they can accelerate component wear and even cause mechanical failure. Therefore, real-time monitoring of the motor's operating status and prompt response to parameter changes are crucial. Adaptive control algorithms can instantly adjust to abnormal fluctuations in equipment operation, ensuring timely optimization of load and speed during the run-in process to avoid damage to other equipment components.

[0080] Step S3 includes the following contents:

[0081] During the run-in process, sensors installed in key components of the equipment monitor operating parameters such as temperature, vibration, and current in real time. Temperature sensors are located on the windings, bearings, and rotor; vibration sensors are installed on the motor housing and support structure; and current sensors are located at the power input. All sensors transmit data to the control system via wireless or wired channels. To ensure data accuracy, a time synchronization protocol (such as IEEE 1588 PTP) is used to synchronize data from different sensors, ensuring accurate correlation between parameters.

[0082] Before entering the control system, sensor data must be filtered for noise and outliers. A Kalman filter is used to filter temperature, vibration, and current signals in real time, removing external noise or transient interference signals while retaining valid operating data. For current fluctuation signals, a Fourier transform is used to convert the current signal from the time domain to the frequency domain. By analyzing its spectral components, abnormal harmonics or interference signals can be identified.

[0083] After filtering and preprocessing, the real-time sensor data enters the adaptive control algorithm for threshold determination. First, a set of threshold ranges are preset based on the operating characteristics of the equipment. The thresholds include the upper temperature limit, the upper vibration frequency limit, and the safe range of current fluctuations. The specific definitions are as follows:

[0084] Temperature threshold: T max Its value is determined by the material properties and operating load of the equipment. For example, the safety temperature threshold of the winding is set at 130℃.

[0085] Vibration frequency threshold: f max , set according to the mechanical structure of the motor, for example, the vibration frequency should not exceed 100Hz.

[0086] Current fluctuation threshold: THD, which is monitored by the ratio of the peak amplitude to the fundamental amplitude of the current.

[0087] Once any parameter exceeds the preset threshold, the adaptive control logic is activated, which dynamically adjusts the load and speed based on real-time data. The adjustment strategy is based on the type and degree of the parameter exceeding the threshold:

[0088] Temperature exceeds threshold: If the temperature parameter T is greater than the temperature threshold, reduce the load change rate. The adjustment formula is: Among them, α T is the temperature regulation coefficient.

[0089] Vibration frequency exceeds the threshold: If the vibration frequency parameter f is greater than the vibration frequency threshold, adjust the speed adjustment step size to bring the vibration frequency back to a safe range. The adjustment formula is: Among them, α f is the vibration frequency adjustment coefficient.

[0090] Current fluctuation exceeds the threshold: When the current fluctuation exceeds the safe range (for example, THD is greater than 20%), the load change rate is reduced and the speed is appropriately lowered. The adjustment strategy is similar to the treatment method for temperature and vibration.

[0091] After each parameter adjustment, the control system will compare the adjusted load and speed with the actual operating status of the equipment and update the adjustment coefficient of the algorithm (such as α T , α f ) to optimize the accuracy of subsequent adjustments. Using the Q-learning algorithm from reinforcement learning, the optimal adjustment strategy under different load conditions is updated based on the device response after each adjustment, allowing the control system to continuously optimize the adjustment logic during long-term operation.

[0092] By using sensors to collect the device's temperature, vibration spectrum, and current fluctuation data in real time, and using adaptive control algorithms to perform real-time threshold judgment and dynamic adjustment, it is ensured that all parameters of the motor remain within a safe range during the running-in process, avoiding abnormal wear or failure.

[0093] During the motor run-in process, vibration signals are crucial parameters reflecting the equipment's operating status and component run-in. Insufficient run-in between new and old components can lead to abnormal changes in vibration frequency and amplitude, often manifesting in both the time and frequency domains. Relying solely on time or frequency domain analysis can be difficult to capture these complex dynamic signal characteristics, so a comprehensive analysis of the vibration signal requires a combination of wavelet transform and fast Fourier transform (FFT). By extracting the time-frequency characteristics of the vibration signal and applying pattern recognition algorithms, potential anomalies during the run-in process, such as mechanical imbalance, rotor misalignment, looseness, or localized damage, can be effectively detected.

[0094] Step S4 includes the following contents:

[0095] During motor operation, vibration sensors collect real-time mechanical vibration data, recording the vibration waveforms of the equipment under different load conditions. These signals are stored as time-domain data, representing the change in vibration intensity over time. Assume the vibration signal is x(t), where t is time and x is the vibration amplitude. After each load adjustment, the vibration signal must be re-collected to ensure data continuity.

[0096] After the old parts are replaced, the frequency sharpness index and the time series entropy difference index are obtained based on the vibration signals generated by the new parts after installation.

[0097] The logic for obtaining the spectrum sharpness index is:

[0098] First, convert the vibration signal x(t) in the time domain to the frequency domain to obtain the spectral components of the vibration signal. For the vibration signal, define the time domain signal x(t) as:

[0099] Among them, A is the amplitude of the main vibration signal, f0 is the main frequency, A n is the amplitude of other minor frequencies, f n are the secondary frequencies, φ and φ n is the phase angle.

[0100] The spectrum components are obtained through frequency domain transformation, and the spectrum is S(f), where f is the frequency and S(f) is the vibration intensity at frequency f. In this case, the frequency component distribution of the signal is nonlinear.

[0101] The distribution characteristics of the spectrum are quantified and the frequency distribution function F(f) is constructed to reflect the relative distribution of each frequency component. It is defined as follows:

[0102] This formula represents the ratio of the energy at frequency f to the energy of the entire spectrum. max is the highest frequency range of the spectrum, |S(f)| 2 is the energy intensity at frequency f. Through normalization, the total energy distribution is guaranteed to be between 0 and f max between.

[0103] Calculate the spectrum sharpness increment function to reflect the sudden change of local frequency energy in the spectrum. The local energy change rate based on frequency is expressed as follows:

[0104] Where Δf is the frequency interval, representing the rate of change of local energy within the frequency range. If a frequency band experiences a large energy increase, the sharpness increment will be large, reflecting the sharp nature of the spectrum in that band.

[0105] Taking all the frequency increments into account, the spectrum sharpness index SPI is calculated. By integrating the frequency increments and taking the curvature of the local increments into account as a correction term, we get:

[0106] The formula accumulates the sharpness contribution of each frequency band by integration, and the square of the exponent is used to amplify the frequency band with higher sharpness, making the index more sensitive to locally prominent spike signals.

[0107] in, is the second-order derivative at frequency f, reflecting the curvature change of the frequency energy distribution. The introduction of this correction term significantly amplifies frequency segments with sudden energy changes in the vibration signal (such as harmonic components caused by faults), thereby improving the ability to capture abnormal signals.

[0108] After a new component is replaced, the spectrum sharpness index is used to analyze the vibration signals generated during the running-in process between the old and new components, quantify the concentration and sharpness of their spectrum distribution, and evaluate the running-in effect between the components. This index can reflect whether the replaced new component is mismatched with the existing old component or has local anomalies. A larger index indicates a sudden increase in energy in a specific frequency band. This may be due to local anomalies caused by the incompatibility between the mechanical properties of the new component and the old component, such as mechanical imbalance, structural looseness, or excessive local friction, indicating a potential risk of failure. A smaller index indicates that the vibration signals of the new and old components are smoother, the energy distribution is even, the running-in process is stable, and no significant abnormal vibration occurs, indicating that the replaced new component is well matched with the old component.

[0109] Among them, the logic for obtaining the time series entropy difference index is:

[0110] The continuous vibration signal x(t) is converted into a discrete symbol sequence in order to calculate the dynamic characteristics of the symbol sequence.

[0111] The amplitude of the vibration signal is divided into M intervals according to a predetermined threshold value and mapped to a symbol set {s1, s2, ..., s M}:s k =Φ(x(t i ))=s k , when τ k-1 <x(t i )≤τ k ; where τ0, τ1,…, τ M is the partition threshold, satisfying τ0<τ1<…<τ M,k=1,2,…,M。

[0112] According to the statistical characteristics of the vibration signal, the threshold τ is set k :τ k =τ0+k·Δτ; where, Δτ=(τ M -τ0) / M, ensuring that the symbol intervals are of equal width or adjusted according to signal characteristics.

[0113] Using the symbol sequence, the transition probability between symbols is calculated to reflect the dynamic changes of the time series.

[0114] Calculation symbol s i Transfer to symbol s j Number of times Where δ is an indicator function, which takes 1 when the condition is met and 0 otherwise, and T is the length of the symbol sequence.

[0115] Calculate the transition probability matrix Ensure that the transition probability of each symbol satisfies the normalization condition:

[0116] Through the transfer probability matrix, the entropy difference between the current vibration signal and the normal state signal is calculated to obtain the time series entropy difference index. Calculate the current transfer entropy

[0117] Using the vibration signal under normal operating conditions, the corresponding transfer entropy H is calculated using the same method. normal .

[0118] Calculate the time series entropy difference index TSEDI: TSEDI = |HH normal |.

[0119] The entropy difference index reflects the difference between the current signal complexity and the normal state.

[0120] The time series entropy difference index quantifies the change in vibration signal complexity during the run-in period between new and old components, reflecting the difference in complexity between the current vibration signal and the signal under normal operating conditions. A larger index indicates a greater difference in signal complexity between the current and normal states, potentially indicating increased instability during the run-in period and potential abnormal run-in issues between components, such as mechanical imbalance, uneven friction, or localized damage. A smaller index indicates a closer match between the current signal complexity and the normal state, indicating a good match between the new and old components during the run-in period, stable operation, and no significant abnormal vibration characteristics.

[0121] Two gain functions are calculated for the spectrum sharpness index and the time series entropy difference index, respectively, to express their sensitivity to potential anomalies in different dimensions:

[0122] The gain function enhances the abnormal state response of the two and makes the abnormal peak more prominent through the nonlinear form of the function.

[0123] Combine the time-frequency gain functions into a time-frequency gain fusion index

[0124] By combining the gain effects of the sum and square root terms, the potential abnormal signal changes in the time-frequency domain are amplified, which is particularly suitable for the analysis of vibration signals with concentrated frequencies and high time complexity.

[0125] The Time-Frequency Gain Fusion Index (TFGF) is primarily used to evaluate vibration signals after component replacement. By analyzing gain effects in both the frequency and time domains, it can identify and detect potential anomalies during the run-in process. This index rises rapidly when the vibration signal's frequency concentration or temporal complexity significantly changes, indicating possible component mismatch, mechanical imbalance, or localized wear. A higher TFGF index indicates significant anomalies in the frequency and time domain characteristics of the vibration signal during the run-in process. Frequency concentration or a significant increase in temporal complexity may indicate mechanical imbalance, excessive friction, or localized structural looseness. A lower TFGF index indicates relatively stable vibration signals in both the time and frequency dimensions, with even energy distribution, indicating good component run-in and stable equipment operation with no significant anomalies. By simultaneously focusing on both the time and frequency dimensions, this index can comprehensively assess the equipment's operating status, help identify potential vibration anomalies, and provide effective decision support for maintenance personnel, guiding subsequent run-in adjustments or maintenance work.

[0126] The time-frequency gain fusion index is compared with the upper and lower thresholds. If the time-frequency gain fusion index is greater than or equal to the upper threshold, it indicates that there are obvious abnormalities in the running-in process, which may be due to the mismatch between the new and old components, causing mechanical imbalance, structural looseness, or excessive friction. This indicates that the newly replaced components are obviously not suitable for the current use of the equipment, and a maintenance abnormality status is displayed. If the time-frequency gain fusion index is greater than or equal to the lower threshold and less than the upper threshold, although there is no significant abnormality in the vibration signal, there may be potential hidden dangers in the running-in process. Further analysis of the running-in situation is required, and adjustment of operating parameters is required to ensure safety, indicating a maintenance ambiguous status. If the time-frequency gain fusion index is less than the lower threshold, it indicates that the running-in process of the new and old components is stable, the vibration signal is stable, the equipment is in good operating condition, there are no obvious abnormalities, the running-in effect is ideal, and a maintenance normal status is displayed.

[0127] If the maintenance ambiguity status is obtained, it indicates that there are potential risks during the running-in process of the new and old motor components. Although the vibration signal does not show clear abnormalities, it may cause running-in problems or equipment failure in long-term operation. At this time, it is necessary to further adjust the equipment control strategy, especially the controller's PID parameters. By optimizing the equipment's response characteristics to vibration and load changes, the running-in process of the new and old components will be smoother and long-term wear or failure caused by local mismatch will be avoided. At this time, step S5 is executed. The specific processing process is as follows:

[0128] Step S5 includes the following contents:

[0129] Obtain the proportional, integral, and differential parameters of the current controller, monitor the speed, torque, and vibration signal amplitude of the equipment in real time, and analyze the response curve during load changes as a basis for subsequent PID parameter adjustments.

[0130] To address response speed issues during equipment run-in, the vibration signal's time-frequency gain fusion index is adjusted in real time. This adjustment is achieved by multiplying the original proportional coefficient by a factor related to the time-frequency gain fusion index. The magnitude of the factor depends on the index's position between the upper and lower thresholds. This factor increases the proportional coefficient when the time-frequency gain fusion index approaches the upper limit, increasing the system's sensitivity to changes; and decreases the proportional coefficient when the index approaches the lower limit, making the system more stable. The formula is: The device's sensitivity to load changes is dynamically corrected by adjusting the proportional coefficient.

[0131] K p 'Adjusted proportional coefficient. Used to control the response strength of the system to the error.

[0132] K p The original proportional coefficient determines the system's sensitivity to the error signal. The larger the coefficient, the faster the system responds.

[0133] The adjustment coefficient is used to control the adjustment range of the proportional coefficient. This coefficient is set according to the specific equipment characteristics and the needs of the running-in process, and is generally used to increase or decrease the adjustment amount.

[0134] θ low The lower threshold is used to determine whether the vibration signal is within the normal range. If the time-frequency gain fusion index is close to this value, it indicates that the device is operating stably.

[0135] θ high The upper threshold is used to determine the abnormal state of the vibration signal. When the time-frequency gain fusion index approaches or exceeds this value, it indicates that there is a significant abnormality in the system.

[0136] The normalization expression normalizes the position of the current index relative to the upper and lower thresholds to determine the magnitude of the adjustment.

[0137] In response to the phenomenon of equipment error accumulation, the integral coefficient is adjusted to eliminate steady-state errors. The adjustment method is to make additive adjustments to the original integral coefficient based on the total accumulated errors during the operation of the equipment. The larger the accumulated error, the larger the adjustment range of the integral coefficient, thereby gradually eliminating long-term deviations in the system, making the running-in of new and old components smoother, and ensuring the long-term stability of the equipment. The formula is: Ensure that the system can return to the ideal state stably after long-term operation and avoid long-term fluctuations in vibration signals.

[0138] K l 'Adjusted integral coefficient. It is used to eliminate the steady-state error in the system. The error accumulated over a long period of time will be corrected by this coefficient.

[0139] K l The original integral coefficient is used to adjust the error elimination speed of the equipment during long-term operation. The larger the integral coefficient, the faster the steady-state error elimination speed.

[0140] The integral adjustment coefficient determines the adjustment range of the integral term. This parameter is used to dynamically adjust the size of the integral term according to the specific running-in status.

[0141] The cumulative error sum represents the sum of the error values ​​from the initial time τ = 0 to the current time t. The error value e(τ) is the difference between the set value and the actual value.

[0142] T adjust Adjust the time constant to determine the time scale of the integration process and control the speed and amplitude of the integral coefficient adjustment.

[0143] For sudden vibrations or fluctuations in the equipment within a short period of time, the differential coefficient is adjusted to suppress instantaneous overshoot. The adjustment method is to adjust the differential coefficient based on the instantaneous rate of change of the time-frequency gain fusion index. When the vibration signal of the equipment changes rapidly, the differential coefficient is reduced to suppress the fluctuation amplitude, ensure the smooth operation of the equipment, and avoid the impact of short-term fluctuations on the overall running-in process. The formula is: By adjusting the differential term, the impact of short-term fluctuations on system stability can be offset.

[0144] K d 'Adjusted differential coefficient. Used to suppress instantaneous fluctuations in the system and reduce overshoot.

[0145] K dThe original differential coefficient. It is used to control the system's response speed to instantaneous error changes. The larger the differential coefficient, the stronger the system's response to sudden fluctuations.

[0146] The differential adjustment coefficient determines the adjustment amplitude of the differential term and controls the sensitivity of the control system to instantaneous fluctuations of the vibration signal.

[0147] The time rate of change of the time-frequency gain fusion index. This value is used to detect the instantaneous rate of change of the current vibration signal, reflecting the rapid fluctuation of the signal within a short period of time. If this value is large, it indicates that the system is unstable and the differential coefficient needs to be reduced for stable operation.

[0148] The present invention proposes an effective solution for the running-in process of new and old parts of the motor. First, by collecting multi-dimensional performance parameters of new and old parts and constructing a performance characteristic database, the key parameter differences of different parts can be described in detail, especially the factors affecting the running-in effect such as speed, torque and electrical impedance. Secondly, a personalized running-in curve is generated using a support vector machine regression algorithm, and the load change rate, speed adjustment step and running-in time are accurately set according to the actual operating conditions, avoiding the vibration and current fluctuation problems caused by the initial mismatch between the new and old parts. In addition, the temperature, vibration spectrum and current fluctuation of the equipment are monitored in real time to ensure that when any parameter exceeds the threshold during the running-in process, the adaptive control algorithm can automatically adjust the load and speed to ensure a smooth and orderly running-in process. By extracting frequency domain and time domain features, calculating the spectrum sharpness index and time series entropy difference index, and combining the time-frequency gain fusion index, the running-in status is classified and analyzed, which can effectively identify potential abnormalities of the equipment after the new parts are replaced, and present abnormal, ambiguous or normal maintenance status. Especially in ambiguous situations, by adjusting the PID parameters of the equipment controller, the running-in process of new and old components is further optimized, ensuring that the performance of new and old components gradually matches, avoiding long-term performance degradation and equipment stability issues caused by insufficient running-in. This technical solution not only enables real-time monitoring and dynamic adjustment of the running-in period, resolving the shortcomings of existing technologies that ignore the running-in period between new and old components, but also improves the operating stability and long-term reliability of motor equipment by optimizing the running-in process, preventing secondary failures and mechanical damage caused by improper running-in.

[0149] Example 2: Figure 2 The present invention provides an intelligent electrical equipment maintenance task decision support system, which includes: a performance acquisition module, an algorithm processing module, a monitoring and adjustment module, a signal analysis module and a parameter optimization module;

[0150] Performance acquisition module: collects the performance parameters of new and old components, builds a multi-dimensional performance characteristic database, and outputs detailed performance characteristic data of new and old components, which is passed to the algorithm processing module.

[0151] Algorithm processing module: Based on the collected multi-dimensional performance characteristic data, it uses the support vector machine regression algorithm to generate a personalized running-in curve, sets the load change rate, speed adjustment step and running time during the running-in period, outputs the running-in curve and load and speed adjustment plan, and passes it to the monitoring and adjustment module.

[0152] Monitoring and adjustment module: monitors the device's temperature, vibration spectrum, and current fluctuations in real time, uses control algorithms to dynamically adjust the load and speed when the parameters exceed preset thresholds, outputs the device's real-time monitoring data and adjusted operating status, and transmits it to the signal analysis module.

[0153] Signal analysis module: Based on the vibration signal in the monitoring data, it extracts frequency domain and time domain features, calculates the spectrum sharpness index and time series entropy difference index, combines the time-frequency gain fusion index to identify abnormal equipment running-in status, outputs abnormal classification, and passes it to the parameter optimization module.

[0154] Parameter Optimization Module: If an ambiguous state is obtained, the PID parameters of the equipment controller are adjusted to optimize the operating parameters during the running-in process, and the optimized PID parameter settings are output to ensure that the performance of the new and old components gradually matches.

[0155] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0156] The above description is merely illustrative of certain exemplary embodiments of the present invention. It goes without saying that those skilled in the art will be able to modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the scope of protection of the claims.

[0157] It should be noted that, in this document, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises", "includes" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article or device. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0158] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. An intelligent electrical equipment maintenance task decision support method, characterized in that: Including steps: S1, collects performance parameters of new and old components and builds a multi-dimensional performance characteristics database; S2 uses the support vector machine regression algorithm to process multi-dimensional performance characteristic data, generate a personalized running-in curve, and set the load change rate, speed adjustment step size, and operating time during the running-in period; S3 monitors the device's temperature, vibration spectrum, and current fluctuations in real time, and uses control algorithms to dynamically adjust load and speed when parameters exceed thresholds. S4, based on the frequency domain and time domain feature extraction of the vibration signal during the running-in process of the old and new parts, calculates the spectrum sharpness index and time series entropy difference index, and uses the time-frequency gain fusion index to identify potential running-in anomalies after the replacement of new equipment parts, and presents the maintenance abnormality state, ambiguous state or normal maintenance state; The logic for obtaining the spectrum sharpness index is: First, the vibration signal in the time domain Convert to the frequency domain to obtain the spectrum components of the vibration signal; for the vibration signal, let the vibration signal be ,in For time, represents the amplitude of the vibration, expressed as: ;in, is the amplitude of the main vibration signal, is the main frequency, is the amplitude of other minor frequencies, is the secondary frequency, and is the phase angle; The spectrum components are obtained by frequency domain transformation, and the spectrum is ,in is the frequency, is the frequency Vibration intensity under Quantify the distribution characteristics of the spectrum and construct a frequency distribution function , reflects the relative distribution of each frequency component; it is defined as follows: ;in, is the highest frequency range of the spectrum, is the frequency The energy intensity at the location is normalized to ensure that the total energy distribution is between 0 and between; Calculate the spectral sharpness delta function, based on the local energy change rate of frequency, expressed as follows: ;in, is the frequency interval; Taking into account the sharpness increments at all frequencies, the spectrum sharpness index is calculated , by integrating the frequency sharpness increment and incorporating the curvature of the local sharpness increment as a correction term into the calculation, we get: ;in, is the frequency The second-order derivative at reflects the curvature change of the frequency energy distribution; Among them, the logic for obtaining the time series entropy difference index is: The continuous vibration signal Convert to a discrete sequence of symbols; The amplitude of the vibration signal is divided into intervals, mapped to a set of symbols : ,when ;in, is the dividing threshold, satisfying ; Set the threshold according to the statistical characteristics of the vibration signal : ;in, , ensure that the symbol intervals are of equal width or adjusted according to the signal characteristics; Using the symbol sequence, calculate the transition probability between symbols; Calculation symbols Transfer to symbol Number of times : ;in, is an indicator function, which takes 1 when the condition is met and 0 otherwise. is the length of the symbol sequence; Calculate the transition probability matrix : ; Ensure that the transition probability of each symbol meets the normalization condition: ; Through the transfer probability matrix, the entropy difference between the current vibration signal and the normal state signal is calculated to obtain the time series entropy difference index; the current transfer entropy is calculated : ; Using the vibration signal under normal operating conditions, the corresponding transfer entropy is calculated using the same method ; Calculate the time series entropy difference index : ; Two gain functions are calculated for the spectrum sharpness index and the time series entropy difference index, respectively, to express their sensitivity to potential anomalies in different dimensions: ; ; Combine the time-frequency gain functions into a time-frequency gain fusion index : ; Compare the time-frequency gain fusion index with the upper and lower thresholds; if the time-frequency gain fusion index is greater than or equal to the upper threshold, an abnormal maintenance state is displayed; if the time-frequency gain fusion index is greater than or equal to the lower threshold and less than the upper threshold, an ambiguous maintenance state is displayed; if the time-frequency gain fusion index is less than the lower threshold, a normal maintenance state is displayed; S5, if a maintenance ambiguous state is obtained, the PID parameters of the equipment controller are adjusted to optimize the operating parameters during the running-in process.

2. The intelligent electrical equipment maintenance task decision support method according to claim 1, characterized in that: Step S2 includes the following contents: First, the multi-dimensional performance data collected in step S1 is standardized; The standardized multi-dimensional performance data is used as the training dataset, the target output value is the running-in state of the equipment, and the mean square error is used as the loss function for model training to optimize the parameters and penalty factors of the SVR model. After training is completed, the SVR model predicts the running-in status under different working conditions based on the characteristic data of new and old components; and generates the corresponding running-in curve based on the predicted running-in status.

3. The intelligent electrical equipment maintenance task decision support method according to claim 2, characterized in that: Step S3 includes the following contents: A set of threshold ranges are preset based on the operating characteristics of the equipment. These include upper temperature limits, upper vibration frequency limits, and safe ranges for current fluctuations. Once any parameter exceeds the preset threshold, adaptive control logic is activated, dynamically adjusting the load and speed based on real-time data. Adjustment strategies are executed based on the type and degree of parameter exceeding the threshold.

4. The intelligent electrical equipment maintenance task decision support method according to claim 3, characterized in that: Step S4 includes the following contents: During the operation of the motor, mechanical vibration data is collected in real time and the vibration waveform of the equipment under different load conditions is recorded. After the old parts are replaced, the frequency sharpness index and time series entropy difference index are obtained based on the vibration signals generated by the operation after the new parts are installed.

5. The intelligent electrical equipment maintenance task decision support method according to claim 1, characterized in that: Step S5 includes the following contents: Obtain the proportional, integral, and differential parameters of the current controller, monitor the speed, torque, and vibration signal amplitude of the equipment in real time, and analyze the response curve during load changes as a basis for PID parameter adjustment; To address response speed issues during equipment running-in, the vibration signal's time-frequency gain fusion index is adjusted in real time. This involves multiplying the original proportional coefficient by a factor related to the time-frequency gain fusion index. The size of the factor depends on the index's position between the upper and lower thresholds. In view of the phenomenon of equipment error accumulation, the integral coefficient is adjusted to eliminate the steady-state error. This is to make an additive adjustment to the original integral coefficient based on the total error accumulated during the operation of the equipment. In response to sudden vibrations or fluctuations of the equipment in the short term, the instantaneous overshoot is suppressed by adjusting the differential coefficient. The differential coefficient is adjusted according to the instantaneous change rate of the time-frequency gain fusion index.

6. An intelligent electrical equipment maintenance task decision support system, used to implement the intelligent electrical equipment maintenance task decision support method according to any one of claims 1 to 5, characterized in that: include: Performance acquisition module, algorithm processing module, monitoring and adjustment module, signal analysis module and parameter optimization module; Performance acquisition module: collects the performance parameters of new and old components, builds a multi-dimensional performance characteristic database, outputs detailed performance characteristic data of new and old components, and passes it to the algorithm processing module; Algorithm processing module: Based on the collected multi-dimensional performance characteristic data, it uses the support vector machine regression algorithm to generate a personalized running-in curve, set the load change rate, speed adjustment step size and running time during the running-in period, output the running-in curve and load and speed adjustment plan, and pass it to the monitoring and adjustment module; Monitoring and Adjustment Module: This module monitors the device's temperature, vibration spectrum, and current fluctuations in real time. It uses control algorithms to dynamically adjust the load and speed when these parameters exceed preset thresholds. It then outputs the device's real-time monitoring data and adjusted operating status to the signal analysis module. Signal analysis module: Based on the vibration signals in the monitoring data, it extracts frequency and time domain features, calculates the spectrum sharpness index and time series entropy difference index, and combines the time-frequency gain fusion index to identify abnormal equipment running-in status. It then outputs the abnormality classification and passes it to the parameter optimization module. Parameter Optimization Module: If an ambiguous state is obtained, the PID parameters of the equipment controller are adjusted to optimize the operating parameters during the running-in process, and the optimized PID parameter settings are output to ensure that the performance of the new and old components gradually matches.

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