Motor fault analysis method and system combining high-speed transient data and steady-state data

Abstract

The invention discloses a motor fault analysis method and system combining high-speed transient data and steady-state data, and belongs to the technical field of motor fault early warning. According to the method, motor millisecond-level high-speed transient data and industrial system steady-state data are synchronously collected, a double-platform collaborative analysis architecture is constructed, high-speed collection and data integration are achieved through LabVIEW, feature extraction and model reasoning are executed through Python, and millisecond-level alignment of multi-source data is achieved through an accurate time synchronization mechanism; a sliding window 3 sigma criterion dynamic verification algorithm is innovatively provided, and interference data are eliminated through cross comparison of a high-speed calculation steady-state value and original steady-state data; based on a quartile IQR algorithm, multi-time-dimension dynamic threshold analysis is carried out on effective transient data, and accurate capture and graded early warning of early fault features are realized, so that the hysteresis bottleneck of traditional steady state monitoring is broken through, and the fault early warning timeliness and the anti-interference capability are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of motor fault early warning, more particularly to a motor fault analysis method and system combining high-speed transient data and steady-state data. BACKGROUND

[0002] In the field of industrial automation, electric motors as the core power equipment, its operation reliability directly affects the stability and energy efficiency level of production system.

[0003] However, the current mainstream motor fault early warning mechanism mainly relies on steady-state monitoring data (such as voltage, current, temperature, vibration amplitude, etc.), which has a significant lag defect. Specifically, steady-state data reflects the average state of long-term operation of the equipment, and is not sensitive to early fault characteristics, resulting in irreversible damage to the motor (such as bearing sintering, turn-to-turn short circuit, etc.) when the early warning is triggered, thereby causing unplanned downtime and high maintenance costs.

[0004] Therefore, how to propose a motor fault analysis method and system combining high-speed transient data and steady-state data to capture early fault signs and improve motor fault early warning efficiency is a problem that needs to be solved by those skilled in the art. SUMMARY

[0005] Therefore, the present application provides a motor fault analysis method and system combining high-speed transient data and steady-state data to improve the accuracy and timeliness of fault early warning.

[0006] In order to achieve the above purpose, the present application adopts the following technical solutions: On the one hand, the present application proposes a motor fault analysis method combining high-speed transient data and steady-state data, comprising the following steps: High-speed real-time acquisition of motor transient signals, and saving as high-speed recorded transient data according to time sequence; Reading the data of the motor measuring instrument and saving as steady-state recorded data according to time; Based on the high-speed recorded transient data, high-speed calculated steady-state data is obtained; According to the data recording time, the high-speed calculated steady-state data and the steady-state recorded data are compared and analyzed, and the available data is determined according to the analysis result; Data classification calculation is performed on the high-speed recorded transient data in the available data to obtain high-speed recorded analysis data; The high-speed recorded analysis data is saved and recorded in time sequence, and the abnormal related data in the high-speed recorded analysis data is counted at intervals; Each type of abnormal data and normal data is weighted and scored, and a motor fault warning score is obtained by weighting. The health status of the motor is judged based on the motor fault warning score.

[0007] Preferably, based on the high-speed recording of transient data Obtain steady-state data after high-speed calculation ,include: The high-speed recorded transient data is divided into time windows, and the high-speed recorded transient data is divided according to the time window. Obtain steady-state data after high-speed calculation The formula is as follows: ; In the formula, N is the total number of sampling points within each statistical window.

[0008] Preferably, the steady-state data after high-speed calculation is processed according to the data recording time. and the steady-state recording data Conduct comparative analysis, and determine the usable data based on the analysis results, including: The statistics for each time window are dynamically calculated, including the window mean. and window standard deviation The formula is as follows: ; ; In the formula, W represents the number of sliding windows. Let be the mean of the k-th sliding window. Let be the mean of the j-th sliding window; The statistic is compared with the steady-state record data within the same time window. Compare and judge ( The question asks whether the standard deviation of the mean of the most recent W windows holds true when sliding to the j-th window. If true, then the question asks whether the high-speed calculated steady-state data for that time window holds true. and the steady-state recording data Data is considered usable if it is not, otherwise it is considered abnormal.

[0009] Preferably, high-speed recording transient data from the available data is classified and calculated to obtain high-speed recording analysis data, including: Three-phase transient voltage data are used to calculate the mean, variance, peak value, and voltage imbalance of transient voltage. Three-phase transient current data are used to calculate the mean, variance, peak value, and THD of transient current. Transient temperature data of the motor is used to calculate the transient temperature rise slope; Transient vibration data of the motor is used to calculate the transient vibration frequency.

[0010] Preferably, the abnormal related data in the high-speed recording analysis data includes: For each type of high-speed record analysis data, the median of the lower half and the median of the upper half are determined as the first quartile Q1 and the third quartile Q3, respectively; The interquartile range (IQR) is calculated based on the first quartile Q1 and the third quartile Q3, using the formula: IQR = Q3 – Q1; Calculate the lower bound: down = Q1 - 1.5 IQR, calculate the upper bound up = Q³ + 1.5 IQR determines the normal value range based on the upper and lower bounds; Determine whether the high-speed recording analysis data is within the normal value range. If it is, it is normal data; otherwise, it is abnormal data. The anomaly-related data includes the number of times anomalies occur, the frequency of anomalies, and the changes in the magnitude of anomalies.

[0011] On the other hand, the present invention also proposes a motor fault analysis system that combines high-speed transient data and steady-state data, which is used to implement the above-mentioned motor fault analysis method that combines high-speed transient data and steady-state data, including a LabVIEW module and a Python analysis platform; The LabVIEW module is responsible for real-time acquisition and local caching of high-speed transient data, as well as obtaining steady-state data through the industrial OPC interface. After being calibrated with a unified timestamp, both the high-speed transient data and the steady-state data are transmitted to the Python analysis platform via the TCP / IP protocol. The Python analysis platform compares and analyzes high-speed recorded transient and steady-state recorded data to obtain fault analysis results; the fault analysis results include motor fault warning scores and motor health status. The fault analysis results are transmitted back to the LabVIEW module via TCP / IP; the LabVIEW module drives the GUI interface based on the received fault analysis results to realize the visualization of fault information.

[0012] Preferably, the Python analysis platform executes the sliding window 3σ criterion dynamic verification algorithm and performs multi-time-dimensional dynamic threshold analysis on the effective transient data using the interquartile range (IQR) algorithm to obtain fault analysis results.

[0013] As can be seen from the above technical solution, compared with the prior art, this invention discloses a motor fault analysis method and system that combines high-speed transient data and steady-state data. It adopts a dual-platform collaborative architecture of LabVIEW and Python, using high-precision sensors and high-speed acquisition modules to collect high-speed transient data such as three-phase voltage, current, vibration, and temperature of the motor in real time and save them over time. Simultaneously, it acquires steady-state recorded data through industrial OPC communication. After reading the two types of data, the Python analysis platform calculates the high-speed transient data into high-speed calculated steady-state data. It uses the sliding window 3σ criterion to compare the two types of steady-state data to determine usable data. It then classifies and calculates the high-speed transient data in the usable data to obtain high-speed recorded analysis data. Anomalies are statistically analyzed using the interquartile range (IQR) algorithm according to different time windows, and then a fault warning score is obtained by weighting the anomalies. Finally, the fault information is presented through the LabVIEW GUI interface. This invention achieves accurate correlation and intelligent analysis of multi-source data through dual-platform collaboration, dynamically eliminates interfering data, accurately extracts fault features, and improves the accuracy and timeliness of fault warnings. Attached Figure Description

[0014] Figure 1 This is a system architecture diagram of the present invention; Figure 2 This is a system software architecture diagram of the present invention; Figure 3 A schematic diagram of steady-state data recording; Figure 4 This is a sample image of transient data; Figure 5 This is a schematic diagram of the steady-state data after high-speed calculation; Figure 6 The diagram shows the interquartile range (IQR) algorithm. Figure 7 This is a comparison chart of the number of outlier data entries. Figure 8 This is a comparison chart of abnormal data frequencies; Figure 9 This is a graph showing the average value of the three-phase transient voltages. Figure 10 This is a graph showing the variance of the three-phase transient voltage. Figure 11 This is a graph showing the peak value of the three-phase transient voltage. Figure 12 This is a voltage imbalance curve. Figure 13 This is a graph showing the average value of the three-phase transient current. Figure 14 This is a graph showing the variance of the three-phase transient current. Figure 15 This is a graph showing the peak value of the three-phase transient current. Figure 16The diagram shows the harmonic distortion rate curves of the three-phase current. Figure 17 This is a graph showing the change in the slope of the transient temperature rise. Figure 18 This is a graph showing the transient vibration frequency change. Detailed Implementation

[0015] 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.

[0016] On the one hand, such as Figure 1 As shown in the figure, this invention proposes a motor fault analysis method that combines high-speed transient data and steady-state data, including the following steps: S1. High-speed real-time acquisition of transient signals from the motor, and saving them as high-speed recorded transient data in chronological order.

[0017] High-precision sensors are installed on the motor side to measure transient signals such as three-phase input voltage, three-phase input current, vibration signal, and temperature in real time. These high-precision sensor signals are then rapidly and in real-time acquired by the sensor data and stored in chronological order as high-speed transient data. Figure 4 .

[0018] S2. Read the data from the motor measuring instrument and save it as steady-state record data according to time, such as... Figure 3 As shown.

[0019] The system reads data from existing motor measuring instruments on-site (including RMS values ​​of motor voltage and motor current) via industrial OPC communication and saves it as steady-state data based on time.

[0020] S3. Based on high-speed recording of transient data, obtain high-speed calculated steady-state data, refer to... Figure 5 ,include: High-speed recording transient data is divided into time windows, and high-speed recording transient data is processed according to the time window. Obtain steady-state data after high-speed calculation The formula is as follows: ; In the formula, N is the total number of sampling points within each statistical window.

[0021] S4. Based on the data recording time, compare and analyze the steady-state data calculated at high speed with the steady-state recorded data, and determine the usable data based on the analysis results, including: The statistics for each time window are dynamically calculated, including the window mean. and window standard deviation The formula is as follows: ; ; In the formula, W represents the number of sliding windows. Let be the mean of the k-th sliding window. Let be the mean of the j-th sliding window; Compare the statistics with the steady-state record data within the same time window. Compare and judge ( To determine if the standard deviation of the mean of the most recent W windows holds true when sliding to the j-th window, if so, then use the high-speed calculated steady-state data for that time window. and steady-state recording data Data is considered usable if it is not, otherwise it is considered abnormal.

[0022] Specifically, this embodiment takes phase A voltage as an example, and the steps are as follows: (1) The high-speed recorded transient data is transformed into high-speed calculated steady-state data through an algorithm, and each group consists of 100 data points as follows: .

[0023] in This is the steady-state voltage data after high-speed calculation.

[0024] Calculate 5 groups, and get: There are a total of 1000 data points.

[0025] (2) Linear interpolation of the original steady-state data can yield the same amount of data as the steady-state data after high-speed calculation (see Table 1), as follows: ,in For the interpolated steady-state data, the interpolation point is 0.00495. Calculate 5 sets, and you will get... There are a total of 1000 data points.

[0026] Table 1. Original data for linear interpolation

[0027] (3) Then, the difference between the interpolated steady-state recorded data and the high-speed calculated steady-state data is calculated. As follows: The remaining differences are -5.719, 1.834, 2.527, and 2.393, respectively, representing a total of 1000 differences.

[0028] (4) Calculate the mean and standard deviation for every 5 differences. See below: , .

[0029] (5) Then calculate the upper and lower bounds of the 3σ criterion. As follows: Upper Realm The lower realm .

[0030] (6) Determine whether the difference is within the range.

[0031]

[0032] If all five differences are within the range, the data is usable; if any one of them is outside the range, the data is unusable and should be discarded.

[0033] S5. High-speed recording transient data from the available data is classified and calculated to obtain high-speed recording analysis data, including: Three-phase transient voltage data are used to calculate the mean, variance, peak value, and voltage imbalance of transient voltage. Three-phase transient current data are used to calculate the mean, variance, peak value, and current (harmonic distortion rate) THD of transient current. Transient temperature data of the motor is used to calculate the transient temperature rise slope; Transient vibration data of the motor is used to calculate the transient vibration frequency.

[0034] S6. Save the high-speed recording analysis data in chronological order, and at regular intervals, statistically analyze the abnormal related data in the high-speed recording analysis data.

[0035] In this embodiment, statistical analysis is performed every hour, every 4 hours, every 8 hours, every 12 hours, and every 24 hours to record abnormal data, including the occurrence of abnormal data, the number of times abnormal data occurs, the frequency of abnormal data, and the changes in the amplitude of abnormal data.

[0036] This embodiment uses the interquartile range (IQR) algorithm to identify abnormal data.

[0037] The interquartile range (IQR) is a measure of variability achieved by dividing a dataset into quartiles. Quartiles divide a rank-sorted dataset into four equal parts: Q1 (the first quartile), Q2 (the second quartile), and Q3 (the third quartile). The IQR is defined as Q3–Q1, and data points outside the range of Q3+1.5IQR or Q1-1.5IQR are considered outliers. Figure 6 The diagram shows the specific algorithm.

[0038] The specific algorithm steps are as follows: For each type of high-speed record analysis data, the median of the lower half and the median of the upper half are determined as the first quartile Q1 and the third quartile Q3, respectively; The interquartile range (IQR) is calculated based on the first quartile Q1 and the third quartile Q3, using the formula: IQR = Q3 – Q1; Calculate the lower bound: down = Q1 - 1.5 IQR, calculate the upper bound up = Q³ + 1.5 IQR determines the normal value range based on the upper and lower bounds; Determine whether the high-speed recording analysis data is within the normal value range. If it is, it is normal data; otherwise, it is abnormal data. Anomaly-related data includes the number of times anomaly data occurs ( Figure 7 ), frequency of abnormal data ( Figure 8 ), abnormal data amplitude changes.

[0039] This embodiment uses hourly data as an example to identify abnormal data, as detailed below: Average phase A voltage: Q1 is the median of the lower half, specifically 0.154; Q3 is the median of the upper half, specifically 0.378; IQR = Q3 - Q1 = 0.224; lower bound down = Q1 - 1.5. IQR = -0.183, upper bound up = Q³ + 1.5 IQR = 0.714.

[0040] Phase A voltage variance: Q1 is the median of the lower half, specifically 144761.478; Q3 is the median of the upper half, specifically 144809.238; IQR = Q3 - Q1 = 47.760; lower bound down = Q1 - 1.5 IQR = 144689.838, upper bound up = Q³ + 1.5 IQR=144880.878.

[0041] Phase A voltage peak value: Q1 is the median of the lower half, specifically 552.787; Q3 is the median of the upper half, specifically 561.134; IQR = Q3 - Q1 = 8.347; lower bound down = Q1 - 1.5. IQR = 540.267, upper bound up = Q³ + 1.5 IQR = 573.655.

[0042] The average voltage of phase B: Q1 is the median of the lower half, 0.139, and Q3 is the median of the upper half, 0.391. IQR = Q3 - Q1 = 0.252, and the lower bound down = Q1 - 1.5. IQR = -0.239, upper bound up = Q³ + 1.5 IQR = 0.769.

[0043] Phase B voltage variance: Q1 is the median of the lower half, specifically 144766.021; Q3 is the median of the upper half, specifically 144812.122; IQR = Q3 - Q1 = 46.101; lower bound down = Q1 - 1.5 IQR = 144696.869, upper bound up = Q³ + 1.5 IQR=144881.274.

[0044] Phase B voltage peak values: Q1 is the median of the lower half, specifically 552.979; Q3 is the median of the upper half, specifically 563.511; IQR = Q3 - Q1 = 10.532; lower bound down = Q1 - 1.5. IQR = 537.181, upper bound up = Q³ + 1.5 IQR = 579.309.

[0045] The average voltage of phase C: Q1 is the median of the lower half, with a value of 0.140; Q3 is the median of the upper half, with a value of 0.383; IQR = Q3 - Q1 = 0.243; lower bound down = Q1 - 1.5. IQR = -0.224, upper bound up = Q³ + 1.5 IQR = 0.747.

[0046] C-phase voltage variance: Q1 is the median of the lower half, specifically 144760.316; Q3 is the median of the upper half, specifically 144797.043; IQR = Q3 - Q1 = 36.727; lower bound down = Q1 - 1.5 IQR = 144705.225, upper bound up = Q³ + 1.5 IQR=144852.133.

[0047] C-phase voltage peak value: Q1 is the median of the lower half, specifically 548.688; Q3 is the median of the upper half, specifically 559.805; IQR = Q3 - Q1 = 11.117; lower bound down = Q1 - 1.5. IQR = 532.013, upper bound up = Q³ + 1.5 IQR = 576.480.

[0048] Voltage imbalance: Q1 is the median of the lower half, with a value of 0.010; Q3 is the median of the upper half, with a value of 0.017; IQR = Q3 - Q1 = 0.008; lower bound down = Q1 - 1.5 IQR = -0.002, upper bound up = Q³ + 1.5 IQR=0.028.

[0049] Average phase A current: Q1 is the median of the lower half, specifically 0.403; Q3 is the median of the upper half, specifically 1.247; IQR = Q3 - Q1 = 0.844; lower bound down = Q1 - 1.5. IQR = -0.862, upper bound up = Q³ + 1.5 IQR = 2.513.

[0050] Phase A current variance: Q1 is the median of the lower half, specifically 2522.422; Q3 is the median of the upper half, specifically 2530.804; IQR = Q3 - Q1 = 8.382; lower bound down = Q1 - 1.5 IQR = 2509.850, upper bound up = Q³ + 1.5 IQR = 2543.377.

[0051] Peak current of phase A: Q1 is the median of the lower half, specifically 119.063; Q3 is the median of the upper half, specifically 128.659; IQR = Q3 - Q1 = 9.596; lower bound down = Q1 - 1.5. IQR = 104.669, upper bound up = Q³ + 1.5 IQR = 143.053.

[0052] Phase A current THD: Q1 is the median of the lower half, specifically 4.982; Q3 is the median of the upper half, specifically 5.093; IQR = Q3 - Q1 = 0.111; lower bound down = Q1 - 1.5. IQR = 4.816, upper bound up = Q³ + 1.5 IQR = 5.259.

[0053] The average B-phase current is as follows: Q1 is the median of the lower half, with a value of 0.402; Q3 is the median of the upper half, with a value of 1.248; IQR = Q3 - Q1 = 0.846; and the lower bound down = Q1 - 1.5. IQR = -0.867, upper bound up = Q³ + 1.5 IQR = 2.517.

[0054] Phase B current variance: Q1 is the median of the lower half, specifically 2524.824; Q3 is the median of the upper half, specifically 2530.700; IQR = Q3 - Q1 = 5.876; lower bound down = Q1 - 1.5 IQR = 2516.009, upper bound up = Q³ + 1.5 IQR = 2539.514.

[0055] Peak B-phase current: Q1 is the median of the lower half, specifically 118.285; Q3 is the median of the upper half, specifically 126.288; IQR = Q3 - Q1 = 8.004; lower bound down = Q1 - 1.5. IQR = 106.279, upper bound up = Q³ + 1.5 IQR = 138.293.

[0056] Phase B current THD: Q1 is the median of the lower half, specifically 4.990; Q3 is the median of the upper half, specifically 5.084; IQR = Q3 - Q1 = 0.093; lower bound down = Q1 - 1.5. IQR = 4.850, upper bound up = Q³ + 1.5 IQR = 5.224.

[0057] The average C-phase current is as follows: Q1 is the median of the lower half, with a value of 0.402; Q3 is the median of the upper half, with a value of 1.243; IQR = Q3 - Q1 = 0.841; and the lower bound down = Q1 - 1.5. IQR = -0.860, upper bound up = Q³ + 1.5 IQR = 2.505.

[0058] C-phase current variance: Q1 is the median of the lower half, specifically 2523.224; Q3 is the median of the upper half, specifically 2528.265; IQR = Q3 - Q1 = 5.041; lower bound down = Q1 - 1.5 IQR = 2515.662, upper bound up = Q³ + 1.5 IQR = 2535.827.

[0059] C-phase current peak value: Q1 is the median of the lower half, specifically 115.261; Q3 is the median of the upper half, specifically 127.380; IQR = Q3 - Q1 = 12.119; lower bound down = Q1 - 1.5. IQR = 97.082, upper bound up = Q³ + 1.5 IQR = 145.559.

[0060] C-phase current THD: Q1 is the median of the lower half, specifically 4.985; Q3 is the median of the upper half, specifically 5.078; IQR = Q3 - Q1 = 0.093; lower bound down = Q1 - 1.5 IQR = 4.846, upper bound up = Q³ + 1.5 IQR = 5.217.

[0061] Transient temperature rise slope: Q1 is the median of the lower half, with a specific value of -0.043; Q3 is the median of the upper half, with a specific value of 0.053; IQR = Q3 - Q1 = 0.096; lower bound down = Q1 - 1.5 IQR = -0.187, upper bound up = Q³ + 1.5 IQR = 0.197.

[0062] Transient oscillation frequencies: Q1 is the median of the lower half, specifically 16.000 Hz; Q3 is the median of the upper half, specifically 38.250 Hz; IQR = Q3 - Q1 = 22.250 Hz; lower bound down = Q1 - 1.5. IQR = -17.375, upper bound up = Q³ + 1.5 IQR = 71.625.

[0063] Based on Table 1 and the above calculation process, taking the transient voltage peak values ​​of the hourly abnormal data (60 data points in total) as an example, the A-phase voltage peak values ​​are 579.01 and 540.01, while the upper limit of the A-phase voltage peak value is 573.655 and the lower limit is 540.267. Both values ​​are outside the range and are therefore abnormal. The B-phase voltage peak value is 582.02, while the upper limit of the B-phase voltage peak value is 579.309 and the lower limit is 537.181. This value is outside the range and is therefore abnormal. In summary, the number of abnormal transient voltage peak values ​​is 3, with a frequency of 5%. The rest follow the same calculation steps and conclusions as above.

[0064] Tables 2, 3, 4, 5, and 6 present abnormal data statistics for each hour, every 4 hours, every 8 hours, every 12 hours, and every 24 hours, respectively. Because the amount of data varies across time windows, the thresholds (upper and lower bounds) for each parameter differ for each time window. This means that data displayed as abnormal in time window A may appear normal in time window B, as a larger time window provides a more comprehensive view of the data distribution. If the data distribution shifts within a time window (e.g., due to equipment operating status switching or seasonal fluctuations), the dynamic threshold can adjust accordingly, avoiding high false alarms or false negatives caused by static thresholds. Furthermore, the upper and lower limit widths can be adjusted based on the current distribution width and skewness, preventing a large number of normal fluctuations from being marked as abnormal during periods of fluctuation.

[0065] Table 2. Hourly Abnormal Data Statistics (60 data points in total)

[0066] Table 3. Statistics of abnormal data every 4 hours (240 data points in total)

[0067] Table 4. Statistics of abnormal data every 8 hours (480 data points in total)

[0068] Table 5. Statistics of abnormal data every 12 hours (720 data points in total)

[0069] Table 6. Statistics of abnormal data every 24 hours (1000 data points in total)

[0070] Figure 7 This is a chart comparing the number of outlier data entries. Figure 8Table 7 shows a comparison of the frequency of abnormal data and the amplitude range of abnormal data. Since the IQR algorithm is a dynamic threshold, the threshold for each time window is different, so the abnormal data will also be different.

[0071] Table 7 Comparison of Abnormal Data Amplitude Ranges

[0072] Figures 9-18 A graph of all parameters for the first ten available data points.

[0073] S7. Assign weights to each type of abnormal data and normal data, and calculate the motor fault warning score by weighting the scores. Determine the motor health status based on the motor fault warning score.

[0074] This embodiment takes abnormal data every 24 hours as an example. Table 8 shows the weights of each parameter in the abnormal data every 24 hours.

[0075] Table 8 Weights of Each Parameter

[0076] The total probability of outliers is 0.051 + 0.028 + 0.016 + 0.048 + 0.024 + 0.028 + 0.008 = 0.203, so the total probability of normal data is 1 - 0.203 = 0.797, and the weight of the total normal data is 60. Therefore, the total score is as follows: 95 0.051+95 0.028+95 0.016+95 0.048+95 0.024+95 0.028+90 0.008+60 0.797 = 67.065 > 60. Conclusion: The motor is not faulty.

[0077] On the other hand, this invention also proposes a motor fault analysis system that combines high-speed transient data and steady-state data, used to implement the aforementioned motor fault analysis method that combines high-speed transient data and steady-state data, such as... Figure 1 As shown, the system includes a LabVIEW module and a Python analysis platform; its software architecture is as follows: Figure 2 As shown.

[0078] The LabVIEW module is responsible for the real-time acquisition and local caching of high-speed transient data, as well as the acquisition of steady-state data through the industrial OPC interface. After being calibrated with a unified timestamp, both the high-speed transient data and the steady-state data are transmitted to the Python analysis platform via the TCP / IP protocol. The Python analysis platform compares and analyzes high-speed recorded transient and steady-state data to obtain fault analysis results. Specifically, the platform executes a sliding window 3σ criterion dynamic verification algorithm and uses the interquartile range (IQR) algorithm to perform multi-time-dimensional dynamic threshold analysis on the effective transient data to obtain fault analysis results. These results include motor fault warning scores and motor health status. The fault analysis results are transmitted back to the LabVIEW module via TCP / IP; the LabVIEW module drives the GUI interface based on the received fault analysis results to realize the visualization of fault information.

[0079] 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 apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0080] 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 fault analysis combining high-speed transient data and steady-state data, characterized in that, Includes the following steps: High-speed real-time acquisition of transient signals from the motor is performed and the data is saved as high-speed transient data in chronological order. Read the data from the motor measuring instrument and save it as steady-state record data according to time; Based on the high-speed recorded transient data, the steady-state data after high-speed calculation is obtained; Based on the data recording time, the steady-state data calculated at high speed and the steady-state recorded data are compared and analyzed, and the usable data is determined based on the analysis results; High-speed recording transient data is obtained by performing data classification calculations on the available data; The high-speed recording and analysis data is saved in chronological order, and abnormal related data in the high-speed recording and analysis data are statistically analyzed at regular intervals. Each type of abnormal data and normal data is weighted and scored, and a motor fault warning score is obtained by weighting. The health status of the motor is judged based on the motor fault warning score.

2. The motor fault analysis method combining high-speed transient data and steady-state data according to claim 1, characterized in that, Based on the high-speed recording transient data Obtain steady-state data after high-speed calculation ,include: The high-speed recorded transient data is divided into time windows, and the high-speed recorded transient data is divided according to the time window. Obtain steady-state data after high-speed calculation .

3. The motor fault analysis method combining high-speed transient data and steady-state data according to claim 2, characterized in that, steady-state data The calculation formula is as follows: ； In the formula, N is the total number of sampling points within each statistical window.

4. The motor fault analysis method combining high-speed transient data and steady-state data according to claim 3, characterized in that, Based on the data recording time, the steady-state data after high-speed calculation and the steady-state recording data Conduct comparative analysis, and determine the usable data based on the analysis results, including: The statistics for each time window are dynamically calculated, including the window mean. and window standard deviation ; The statistic is compared with the steady-state record data within the same time window. Compare and judge If the condition is met, then the high-speed calculated steady-state data within that time window will be used. and the steady-state recording data Data is considered usable if it is not; otherwise, it is considered abnormal. in, Let be the mean of the j-th sliding window. Let W be the standard deviation of the mean of the most recent W windows when sliding to the j-th window.

5. The motor fault analysis method combining high-speed transient data and steady-state data according to claim 4, characterized in that, The formula for calculating the statistic is as follows: ； ； In the formula, W represents the number of sliding windows. Let be the mean of the k-th sliding window.

6. The motor fault analysis method combining high-speed transient data and steady-state data according to claim 1, characterized in that, High-speed recording transient data is obtained by performing data classification calculations on the high-speed recording transient data in the available data, including: Three-phase transient voltage data are used to calculate the mean, variance, peak value, and voltage imbalance of transient voltage. Three-phase transient current data are used to calculate the mean, variance, peak value, and THD of transient current. Transient temperature data of the motor is used to calculate the transient temperature rise slope; Transient vibration data of the motor is used to calculate the transient vibration frequency.

7. The motor fault analysis method combining high-speed transient data and steady-state data according to claim 1, characterized in that, The statistical analysis of the high-speed recorded data includes anomaly-related data, including: For each type of high-speed record analysis data, the median of the lower half and the median of the upper half are determined as the first quartile Q1 and the third quartile Q3, respectively; The interquartile range (IQR) is calculated based on the first quartile Q1 and the third quartile Q3, using the formula: IQR = Q3 – Q1; Calculate the lower bound: down = Q1 - 1.5 IQR, calculate the upper bound up = Q³ + 1.5 IQR determines the normal value range based on the upper and lower bounds; Determine whether the high-speed recording analysis data is within the normal value range. If it is, it is normal data; otherwise, it is abnormal data.

8. The motor fault analysis method combining high-speed transient data and steady-state data according to claim 7, characterized in that, The anomaly-related data includes the number of times anomalies occur, the frequency of anomalies, and the changes in the magnitude of anomalies.

9. A motor fault analysis system combining high-speed transient data and steady-state data, characterized in that, A motor fault analysis method combining high-speed transient data and steady-state data as described in any one of claims 1-8, comprising a LabVIEW module and a Python analysis platform; The LabVIEW module is responsible for real-time acquisition and local caching of high-speed transient data, as well as obtaining steady-state data through the industrial OPC interface. After being calibrated with a unified timestamp, both the high-speed transient data and the steady-state data are transmitted to the Python analysis platform via the TCP / IP protocol. The Python analysis platform compares and analyzes high-speed recorded transient data and steady-state recorded data to obtain fault analysis results. The fault analysis results include motor fault warning scores and motor health status. The fault analysis results are transmitted back to the LabVIEW module via TCP / IP. The LabVIEW module drives the GUI interface based on the received fault analysis results, enabling the visualization of fault information.

10. A motor fault analysis system combining high-speed transient data and steady-state data according to claim 9, characterized in that, The Python analysis platform executes the sliding window 3σ criterion dynamic verification algorithm and uses the interquartile range (IQR) algorithm to perform multi-time-dimensional dynamic threshold analysis on the effective transient data to obtain fault analysis results.

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