Data prediction method and system for thermal power plant equipment

Abstract

The invention discloses a data prediction method and system for thermal power plant equipment, particularly relates to the technical field of power equipment state monitoring, and is used for solving the problem of health state misjudgment caused by confusion of working condition noise and fault signal characteristics under a dynamic working condition in existing thermal power plant equipment prediction. Multi-source operation data are collected and classified into a load working condition interval, and time-frequency domain features are extracted to construct a working condition feature set; marking an abnormal working condition interval based on load switching frequency dynamic detection and cross-working-condition consistency analysis; decomposing non-abnormal working condition features in combination with a historical degradation stage matching result to generate a degradation feature subset; degeneration characteristics are corrected through coupling of mechanical dynamics and thermodynamic parameters; and finally, reversely positioning fault source equipment along the fault propagation map, and generating a maintenance instruction. Dynamic working condition feature separation and physical mechanism fusion are realized, and thermal power plant equipment degradation prediction accuracy and maintenance decision reliability are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power equipment status monitoring, and more specifically, to a data prediction method and system for thermal power plant equipment. Background Art

[0002] As the core energy supply unit of the power system, the predictive health management of key rotating equipment (such as steam turbines, boiler feed pumps, fans, etc.) of thermal power plants is a key direction of the industry's intelligent transformation; the increasingly frequent peak-shaving demand of the power grid requires the equipment to dynamically switch operation within the load range, resulting in complex non-steady-state characteristics of the monitoring parameters (vibration, temperature, pressure, etc.); the existing technology mainly constructs a health assessment model through multi-source sensor data fusion, and relies on threshold alarms, statistical filtering and other methods to identify equipment degradation trends. However, on the one hand, the operating noise caused by load fluctuations is highly coupled with the actual degradation signal of the equipment in the time and frequency domain, which is difficult to separate through conventional feature extraction methods; on the other hand, the data-driven model lacks embedded constraints on the physical mechanism of the equipment, resulting in the prediction results may deviate from the actual operating rules.

[0003] In the prior art, the prediction method for thermal power plant equipment has the defect of feature confusion under dynamic conditions. Specifically, the drastic fluctuations in the operating parameters of thermal power plant equipment during the peak-shaving process will mask the true degradation characteristics and cannot effectively distinguish between operating noise and fault signals. This feature confusion phenomenon will lead to misjudgment of the health status of thermal power plant equipment, causing the risk of false alarms or missed detections, thereby affecting the accuracy and reliability of predictive maintenance of thermal power plant equipment. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a data prediction method and system for thermal power plant equipment to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: The data prediction method for thermal power plant equipment includes the following steps: S1. Collect and pre-process the operating status data of thermal power plant equipment; S2. Classify the operating status data according to the preset load condition interval, extract the time-frequency domain features within each load condition interval, and generate a condition feature set; S3, detecting the switching frequency of the load condition interval, and if the switching frequency exceeds the switching frequency threshold, starting the dynamic analysis strategy switching to mark the abnormal condition interval; S4. Decomposing the operating condition feature set of the non-abnormal operating condition interval according to the similarity matching results between the historical degradation stage of the equipment and the current time-frequency domain features to generate an equipment degradation feature subset; S5. Based on the coupling relationship between the mechanical dynamic parameters and thermodynamic parameters of the equipment, parameter coupling correction is performed on the equipment degradation feature subset; S6. Based on the corrected equipment degradation feature subset, trace back the potential fault source equipment along the equipment fault propagation map, determine the maintenance priority and generate maintenance instructions.

[0006] In a preferred embodiment, collecting and preprocessing the operating status data of thermal power plant equipment includes: Synchronously collect vibration data, temperature data and pressure data of thermal power plant equipment, and align the timestamps of vibration data, temperature data and pressure data; Data cleaning is performed on the vibration data, temperature data and pressure data after time stamp alignment. Data cleaning includes using a sliding window filtering method to remove high-frequency noise components in the vibration data and removing abnormal outliers based on the correlation between temperature data and pressure data. The vibration data, temperature data and pressure data after cleaning are resampled according to the preset sampling frequency to generate a standardized operating status data set.

[0007] In a preferred embodiment, the operating status data is classified according to the preset load condition intervals, and the time-frequency domain features in each load condition interval are extracted to generate a condition feature set, including: The standardized operating status data set is divided into multiple load condition intervals according to the load value, and the load condition intervals include a low load interval, a medium load interval and a high load interval; Extract time-frequency domain features from vibration data, temperature data and pressure data in each load condition interval; The time-frequency domain features within the same load condition interval are merged according to the time window to generate a multi-dimensional condition feature set.

[0008] In a preferred embodiment, the time-frequency domain characteristics of vibration data include vibration spectrum energy distribution, time-domain root mean square value and frequency-domain center of gravity frequency, the time-frequency domain characteristics of temperature data include temperature change rate and temperature fluctuation amplitude, and the time-frequency domain characteristics of pressure data include pressure fluctuation frequency and pressure gradient.

[0009] In a preferred embodiment, the switching frequency of the load condition interval is detected, and if the switching frequency exceeds the switching frequency threshold, the dynamic analysis strategy switching is initiated to mark the abnormal condition interval, including: The number of switching times in the load condition interval is counted based on the preset time window, the switching frequency per unit time is calculated, and it is determined whether the switching frequency exceeds the switching frequency threshold; If the switching frequency exceeds the switching frequency threshold, the time-frequency domain features of adjacent load condition intervals are aligned through the dynamic time warping method, and the cross-condition consistency index is extracted; If the cross-operating condition consistency index is lower than the consistency threshold, the load operating condition interval corresponding to the current time window is marked as an abnormal operating condition interval, and the re-extraction of the time-frequency domain features in the abnormal operating condition interval is triggered; otherwise, it is marked as a non-abnormal operating condition interval.

[0010] In a preferred embodiment, extracting the cross-operating condition consistency index includes: aligning the time-frequency domain features of adjacent load condition intervals, calculating the Pearson correlation coefficient for the aligned vibration spectrum energy distribution, temperature change rate and pressure gradient, and taking the average value of the Pearson correlation coefficient as the cross-operating condition consistency index.

[0011] In a preferred embodiment, the operating condition feature set of the non-abnormal operating condition interval is decomposed according to the similarity matching result between the historical degradation stage of the equipment and the current time-frequency domain features to generate an equipment degradation feature subset, including: For the current time-frequency domain features in the working condition feature set of the non-abnormal working condition interval, the matching degree between the current time-frequency domain features and the historical degradation stage of the equipment is determined according to the similarity calculation result between the historical degradation stage label of the equipment and the current time-frequency domain features; Among them, the equipment historical degradation stage label is divided by the vibration amplitude trend and temperature gradient trend in the equipment historical operation data; If the matching degree is greater than or equal to a preset matching threshold, a subset of equipment degradation features is extracted from the operating condition feature set of the non-abnormal operating condition interval using a low-frequency narrowband decomposition mode; If the matching degree is less than the preset matching threshold, based on the correlation between the harmonic component distribution of the vibration spectrum in the operating condition feature set of the non-abnormal operating condition interval and the critical speed of the rotor dynamic parameters, the frequency band where the harmonic energy is concentrated is selected for enhanced decomposition to generate a subset of equipment degradation features.

[0012] In a preferred embodiment, based on the coupling relationship between the mechanical dynamic parameters and the thermodynamic parameters of the equipment, parameter coupling correction is performed on the equipment degradation feature subset, including: According to the correlation between the rotor speed and the vibration amplitude in the mechanical dynamics parameters of the equipment, the amplitude-frequency characteristics of the vibration amplitude in the equipment degradation feature subset are corrected; Based on the matching relationship between the thermal expansion coefficient and the temperature gradient in the thermodynamic parameters, thermal stress compensation correction is performed on the temperature gradient in the equipment degradation feature subset; The corrected vibration amplitude and temperature gradient are combined with the pressure gradient features to generate a parameter-coupled corrected device degradation feature subset.

[0013] In a preferred embodiment, based on the modified equipment degradation feature subset, the potential fault source equipment is traced back along the equipment fault propagation map, the maintenance priority is determined and the maintenance instruction is generated, including: Obtain the equipment fault propagation graph, which is constructed based on the mechanical connection relationship and energy transfer path of the equipment, and includes the fault propagation direction and weight between equipment nodes; Extract abnormal parameters from the equipment degradation feature subset after parameter coupling correction, and match the corresponding nodes in the equipment fault propagation graph according to the abnormal parameter type; Traverse reversely along the fault propagation direction of the equipment fault propagation graph to locate the potential fault source equipment nodes of the abnormal parameters; Determine the maintenance priority according to the number and position depth of the abnormal parameters of the potential fault source equipment nodes; Generate a maintenance instruction including the fault source equipment identification and maintenance time based on the maintenance priority and the remaining service life of the equipment.

[0014] On the other hand, the present invention provides a data prediction system for thermal power plant equipment, including: Data acquisition and processing module: Collect and preprocess the operation status data of thermal power plant equipment; Operating condition classification and extraction module: Classify the operation status data according to the preset load operating condition interval, extract the time-frequency domain features in each load operating condition interval, and generate an operating condition feature set; Dynamic strategy anomaly marking module: Detect the switching frequency of the load operating condition interval. If the switching frequency exceeds the switching frequency threshold, start the dynamic analysis strategy switching to mark the abnormal operating condition interval; Degradation feature decomposition module: Decompose the operating condition feature set of the non-abnormal operating condition interval according to the similarity matching result between the equipment historical degradation stage and the current time-frequency domain features to generate an equipment degradation feature subset; Parameter coupling correction module: Based on the coupling relationship between the mechanical dynamics parameters and thermodynamic parameters of the equipment, perform parameter coupling correction on the equipment degradation feature subset; Fault backtracking decision module: Based on the corrected equipment degradation feature subset, backtrack the potential fault source equipment reversely along the equipment fault propagation graph, determine the maintenance priority and generate a maintenance instruction.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. Through the separation of dynamic operating condition characteristics and the integration of physical mechanisms, the accuracy of thermal power plant equipment health status assessment and the reliability of maintenance decisions are significantly improved; based on the dynamic classification of load condition intervals and the time-frequency domain feature decomposition technology, the operating condition noise and the real degradation signal of the equipment during the peak load regulation process are effectively distinguished, avoiding the misjudgment problem caused by feature confusion under dynamic conditions; by detecting the load switching frequency and triggering the abnormal marking mechanism, it can actively eliminate data interference caused by sudden changes in operating conditions, and at the same time, combined with the similarity matching of historical degradation stages, the feature decomposition mode is adaptively selected to ensure the feature extraction accuracy of different degradation stages, which not only suppresses noise interference, but also realizes the stable capture of degradation characteristics, providing a high-quality data foundation for subsequent analysis; 2. Through the coupling correction of mechanical dynamics and thermodynamic parameters, the physical mechanism of the equipment is embedded in the data-driven model, so that the prediction results are strictly in line with the actual operating laws; then based on the reverse tracing mechanism of the fault propagation map, it is possible to locate the potential fault source equipment from the system level, and generate accurate maintenance instructions based on the priority evaluation of multi-dimensional degradation characteristics. This not only avoids the prediction deviation caused by the traditional model being out of touch with the equipment mechanism, but also optimizes the allocation efficiency of maintenance resources through system-level fault location, ultimately achieving full-link reliability improvement from local anomaly detection to global maintenance decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the data prediction method of thermal power plant equipment of the present invention; Figure 2 It is a structural schematic diagram of the data prediction system of the thermal power plant equipment of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0018] Embodiment 1: Figure 1 A data prediction method for thermal power plant equipment of the present invention is provided, comprising the following steps: S1. Collect and pre-process the operating status data of thermal power plant equipment; S2. Classify the operating status data according to the preset load condition interval, extract the time-frequency domain features within each load condition interval, and generate a condition feature set; S3, detecting the switching frequency of the load condition interval, and if the switching frequency exceeds the switching frequency threshold, starting the dynamic analysis strategy switching to mark the abnormal condition interval; S4. Decomposing the operating condition feature set of the non-abnormal operating condition interval according to the similarity matching results between the historical degradation stage of the equipment and the current time-frequency domain features to generate an equipment degradation feature subset; S5. Based on the coupling relationship between the mechanical dynamic parameters and thermodynamic parameters of the equipment, parameter coupling correction is performed on the equipment degradation feature subset; S6. Based on the corrected equipment degradation feature subset, trace back the potential fault source equipment along the equipment fault propagation map, determine the maintenance priority and generate maintenance instructions.

[0019] S1. Collecting the operating status data of thermal power plant equipment and preprocessing the data are specifically implemented as follows: The vibration data, temperature data and pressure data of the thermal power plant equipment are collected synchronously through vibration sensors, temperature sensors and pressure sensors, and the vibration data, temperature data and pressure data are time-stamp aligned. The vibration sensor adopts a piezoelectric accelerometer, which is installed in the vertical direction of the turbine bearing seat to collect the mechanical vibration signal of the equipment; the temperature sensor adopts a platinum resistance temperature sensor, which is installed in the outlet pipe of the boiler feed pump to monitor the temperature change of the medium; the pressure sensor adopts a capacitive pressure transmitter, which is installed in the steam inlet pipe of the turbine to measure the steam pressure. The specific method of time stamp alignment is to use the collection time point of the vibration data as the reference, and align the collection time points of the temperature data and pressure data to the sampling time of the vibration data through the interpolation algorithm. For example, if the vibration data is collected continuously at time points t1, t2, and t3, and the temperature data is collected at adjacent time points t0, t2, and t4, the interpolation of the temperature data at t1 and t3 is calculated by linear interpolation to ensure that all sensor data are aligned at the same time point.

[0020] Data cleaning is performed on the vibration data, temperature data and pressure data after timestamp alignment. Data cleaning includes the following processing: the sliding window filtering method is used to remove the high-frequency noise components in the vibration data. The window length of the sliding window is determined according to the main frequency period of the equipment vibration signal. For example, when the main frequency is 1kHz, the window length is set to 10 sampling points, and the sliding step is set to 1 sampling point. The vibration data in the window is processed by mean filtering, the average value of the data in the window is calculated, and the high-frequency noise components with a frequency higher than 5kHz are filtered out. The specific method for removing abnormal outliers based on the correlation between temperature data and pressure data is to calculate the Pearson correlation coefficient of temperature data and pressure data in the same time window. If the absolute value of the correlation coefficient is lower than the preset threshold, it is determined that there are abnormal data points in the time window and they are removed. The preset threshold is determined based on historical data statistics. For example, the distribution of the correlation coefficient of temperature and pressure under normal working conditions is analyzed, and the lower limit value covering 95% of normal data is selected as the threshold. For example, the threshold is 0.6. If the absolute value of the correlation coefficient of a time window is lower than 0.6, all data in the time window are removed.

[0021] The vibration data, temperature data and pressure data after cleaning are resampled according to the preset sampling frequency to generate a standardized operating status data set. The preset sampling frequency is set according to twice the maximum vibration frequency of the equipment. For example, if the maximum vibration frequency of the equipment is 5kHz, the sampling frequency is set to 10kHz.

[0022] The resampling method is to interpolate the cleaned data, uniformly sample the vibration data, temperature data and pressure data to the preset frequency, and normalize the data to a numerical range of 0-1 through linear scaling. For example, the original range of the vibration amplitude is -5V to +5V, which is normalized to (original value + 5) / 10, the original range of the temperature data is 0-200℃, which is normalized to the original value / 200, and the original range of the pressure data is 0-10MPa, which is normalized to the original value / 10.

[0023] S2. Classify the operating status data according to the preset load condition interval, extract the time-frequency domain features in each load condition interval, and generate a condition feature set. The specific implementation is as follows: The standardized operating status data set is divided into multiple load condition intervals according to the load value, and the load range of each load condition interval is determined according to the load distribution statistics of the equipment's historical operating data. For example, by analyzing the load value distribution histogram of thermal power plant equipment in the past year, it is determined that the low load interval corresponds to the lowest load range with a higher frequency of load value distribution in the equipment's historical operation, the medium load interval corresponds to the middle load range with a higher frequency of load value distribution, and the high load interval corresponds to the highest load range with a higher frequency of load value distribution. The number of load condition intervals is set according to the peak-shaving requirements of the equipment, for example, divided into three intervals, namely, a low load interval, a medium load interval, and a high load interval.

[0024] The time-frequency domain features are extracted for the vibration data, temperature data and pressure data in each load condition interval. The time-frequency domain features of the vibration data include the vibration spectrum energy distribution, the time domain root mean square value and the frequency domain centroid frequency. The vibration spectrum energy distribution calculates the energy proportion of each frequency band through fast Fourier transform. For example, the vibration data is divided into multiple frequency bands according to the preset frequency resolution, and the energy proportion of each frequency band to the total energy is calculated; the time domain root mean square value is the square root of the square mean of the vibration amplitude in the time window, reflecting the overall energy level of the vibration signal; the frequency domain centroid frequency is the weighted average frequency of the vibration spectrum energy, reflecting the concentrated frequency band of the vibration energy.

[0025] The time-frequency domain characteristics of temperature data include the temperature change rate and the temperature fluctuation amplitude. The temperature change rate is the slope of the temperature change per unit time, for example, obtained by calculating the difference between the temperature values ​​at adjacent time points and dividing by the time interval; the temperature fluctuation amplitude is the difference between the maximum and minimum temperature values ​​in the time window, reflecting the temperature fluctuation range.

[0026] The time-frequency domain characteristics of pressure data include pressure fluctuation frequency and pressure gradient. The pressure fluctuation frequency is the number of times the pressure data exceeds a preset threshold per unit time, for example, the number of times the pressure value exceeds the average pressure value; the pressure gradient is the difference between the pressure values ​​at adjacent time points, reflecting the severity of the pressure change.

[0027] The vibration spectrum energy distribution, time domain root mean square value, frequency domain center of gravity frequency, temperature change rate, temperature fluctuation amplitude, pressure fluctuation frequency and pressure gradient within the same load condition interval are merged according to the time window to generate a multi-dimensional operating condition feature set; the length of the time window is set according to the equipment operation cycle or maintenance requirements, for example, it is set to 10 minutes, which means that a set of multi-dimensional operating condition features is generated every 10 minutes; the merging method is to arrange all time-frequency domain features within the same time window into a feature vector in a preset order, for example, the vibration spectrum energy distribution, time domain root mean square value, frequency domain center of gravity frequency, temperature change rate, temperature fluctuation amplitude, pressure fluctuation frequency and pressure gradient are arranged in sequence to form a feature vector containing multiple dimensions, and each feature vector corresponds to an operating condition within a time window.

[0028] S3. Detect the switching frequency of the load condition interval. If the switching frequency exceeds the switching frequency threshold, start the dynamic analysis strategy switching to mark the abnormal condition interval. The specific implementation is as follows: Based on the preset time window, the number of switching times of the load condition interval is counted, the switching frequency per unit time is calculated, and it is determined whether the switching frequency exceeds the switching frequency threshold. The length of the preset time window is set according to the peak load demand of the equipment. For example, the time window is set to 1 hour, and the number of switching times of the load condition interval within the window is counted. The switching frequency is the number of switching times the length of the time window.

[0029] The switching frequency threshold is determined based on the statistical distribution of load switching frequencies in the historical operation data of the equipment. The specific method is: analyze the load switching records of the equipment in different peak-shaving scenarios in the past year, count the distribution of the number of switches per hour, and select the upper limit of the number of switches covering 95% of normal working conditions as the switching frequency threshold. For example, if the statistical results show that the number of switches per hour under normal conditions does not exceed 5 times, the switching frequency threshold is set to 5 times / hour. If the switching frequency in the current time window exceeds the threshold, it is determined to be a high-frequency switching condition and triggers subsequent analysis.

[0030] If the switching frequency exceeds the switching frequency threshold, the time-frequency domain features of the adjacent load condition intervals are aligned by the dynamic time warping method, and the cross-condition consistency index is extracted. The specific implementation method of the dynamic time warping method is as follows: the vibration spectrum energy distribution, temperature change rate and pressure gradient sequence corresponding to the adjacent load condition intervals are used as inputs, and the time axis is nonlinearly aligned by the dynamic time warping algorithm to eliminate the timing offset caused by the difference in load switching speed. For example, if the duration of the previous load condition interval is 10 minutes, the corresponding vibration spectrum energy distribution sequence contains 10 data points, and the duration of the next load condition interval is 5 minutes, and the corresponding vibration spectrum energy distribution sequence contains 5 data points, then the dynamic time warping algorithm is used to align the two sequences to the same time length, for example, to 10 minutes, and the scaling ratio of the time axis is adjusted during the alignment process so that the key feature points (such as vibration energy peak, temperature change inflection point) match in time. The aligned temperature change rate and pressure gradient sequences use the same time axis adjustment rules to ensure the time synchronization of multi-source signals.

[0031] The calculation method of the cross-operating condition consistency index is: calculate the Pearson correlation coefficient of the aligned vibration spectrum energy distribution, temperature change rate and pressure gradient sequence respectively, and take the average of the three Pearson correlation coefficients as the cross-operating condition consistency index. For example, if the correlation coefficient of the aligned vibration spectrum energy distribution sequence is 0.8, the correlation coefficient of the temperature change rate sequence is 0.7, and the correlation coefficient of the pressure gradient sequence is 0.6, then the cross-operating condition consistency index is (0.8+0.7+0.6) / 3=0.7.

[0032] If the cross-operating condition consistency index is lower than the consistency threshold, the load operating condition interval corresponding to the current time window is marked as an abnormal operating condition interval, and the re-extraction of the time-frequency domain features in the abnormal operating condition interval is triggered.

[0033] The consistency threshold is determined based on the statistical distribution of the cross-condition consistency index in the historical normal condition data. The specific method is: collect the cross-condition consistency index data of the equipment under normal conditions, calculate its mean and standard deviation, and select the mean minus two times the standard deviation as the lower limit of the threshold. For example, if the mean of the cross-condition consistency index under normal conditions is 0.8 and the standard deviation is 0.1, the consistency threshold is set to 0.6. If the current index is lower than 0.6, it is determined to be in the abnormal condition interval.

[0034] The specific operation of triggering re-extraction is: clearing the time-frequency domain feature data in the abnormal working condition interval, and re-extracting the vibration spectrum energy distribution, temperature change rate and pressure gradient characteristics in the interval based on the method of step S2. For example, if a time window is marked as abnormal, the original data of the time window is extracted from the standardized operating status data set, and the load working condition interval classification and time-frequency domain feature extraction are re-executed to generate an updated working condition feature set.

[0035] If the cross-operating condition consistency index is not lower than the consistency threshold, the load operating condition interval corresponding to the current time window is marked as a non-abnormal operating condition interval, the original time-frequency domain feature data is retained and enters the subsequent processing flow.

[0036] S4. Decompose the working condition feature set of the non-abnormal working condition interval according to the similarity matching results between the historical degradation stage of the equipment and the current time-frequency domain features, and generate the equipment degradation feature subset. The specific implementation is as follows: For the current time-frequency domain features in the working condition feature set of the non-abnormal working condition interval, the matching degree between the current time-frequency domain features and the historical degradation stage of the equipment is determined according to the similarity calculation result between the equipment historical degradation stage label and the current time-frequency domain features. The non-abnormal working condition interval is the load working condition interval marked as non-abnormal in step S3, and the working condition feature set is the multi-dimensional feature set generated in step S2, including the vibration spectrum energy distribution, the time domain root mean square value, the frequency domain center of gravity frequency, the temperature change rate, the temperature fluctuation amplitude, the pressure fluctuation frequency and the pressure gradient.

[0037] The equipment historical degradation stage label is divided by the vibration amplitude trend and temperature gradient trend in the equipment historical operation data. The specific method is: collect the vibration amplitude and temperature gradient data in the equipment historical operation data, calculate the average growth rate of the vibration amplitude and the fluctuation amplitude of the temperature gradient according to the time window, and divide the equipment degradation process into the initial, middle and final stages. For example, when the vibration amplitude growth rate is lower than 5% for three consecutive months, it is marked as the initial degradation stage, when the growth rate is between 5% and 10%, it is marked as the middle degradation stage, and when the growth rate exceeds 10%, it is marked as the final degradation stage; when the temperature gradient fluctuation amplitude is lower than 2℃ / h for three consecutive months, it is marked as the initial degradation stage, when the fluctuation amplitude is between 2℃ / h and 5℃ / h, it is marked as the middle degradation stage, and when the fluctuation amplitude exceeds 5℃ / h, it is marked as the final degradation stage.

[0038] If the degree of match is greater than or equal to the preset match threshold, a low-frequency narrowband decomposition mode is used to extract a subset of equipment degradation features from the operating condition feature set of the non-abnormal operating condition interval. The specific implementation method of the low-frequency narrowband decomposition mode is as follows: intercept the frequency band energy of the vibration spectrum energy distribution in the operating condition feature set of the non-abnormal operating condition interval that is lower than the preset cutoff frequency as the equipment degradation feature subset. The preset cutoff frequency is set according to twice the fundamental frequency of the equipment. For example, when the fundamental frequency of the equipment is 50Hz, the cutoff frequency is set to 100Hz, and the vibration spectrum energy distribution in the frequency band from 0Hz to 100Hz is intercepted. The preset matching threshold is determined based on the statistical calculation results of the similarity between the equipment's historical degradation stage label and the corresponding time-frequency domain features. For example, by analyzing the distribution of similarity data under the historical normal degradation stage, the lower limit value covering 95% of the normal data is selected as the threshold. The specific operation is: collect 100 groups of similarity data of the equipment in the initial and intermediate degradation stages, calculate the mean to be 0.8, the standard deviation to be 0.1, and then set the preset matching threshold to 0.7 (mean - 1 times the standard deviation). If the current matching degree is ≥ 0.7, it is judged as the stable degradation stage and triggers low-frequency narrowband decomposition.

[0039] If the matching degree is less than the preset matching threshold, based on the correlation between the harmonic component distribution of the vibration spectrum in the working condition feature set of the non-abnormal working condition interval and the critical speed of the rotor dynamics parameters, the frequency band where the harmonic energy is concentrated is selected for enhanced decomposition to generate a subset of equipment degradation features. The method for selecting the frequency band where the harmonic energy is concentrated is as follows: extract the amplitude of the harmonic component of the vibration spectrum in the working condition feature set of the non-abnormal working condition interval. If the amplitude of a certain harmonic component exceeds the preset ratio of the fundamental frequency amplitude, it is determined to be an energy concentrated frequency band. The preset ratio is determined based on the historical harmonic distribution statistics of the equipment. For example, the ratio of the second harmonic amplitude to the fundamental frequency amplitude in the historical data of the equipment in the terminal degradation stage is statistically calculated, and the lower limit value covering 90% of the normal data is selected as the ratio threshold. If the threshold is 20%, when the second harmonic amplitude exceeds 20% of the fundamental frequency amplitude, the frequency band is selected for enhanced decomposition.

[0040] The critical speed correlation of rotor dynamic parameters is specifically manifested as follows: the speed corresponding to the fundamental frequency is determined according to the rotor critical speed calculation formula. For example, when the critical speed is 3000rpm, the fundamental frequency corresponds to 50Hz, the second harmonic corresponds to 100Hz, and the third harmonic corresponds to 150Hz. Based on this correlation, the frequency band where the harmonic energy is concentrated is located. The specific operation of enhanced decomposition is: bandpass filtering the selected frequency band where the harmonic energy is concentrated, extracting the vibration energy distribution within the frequency band, and merging it with the temperature change rate and pressure gradient characteristics to generate a subset of equipment degradation characteristics.

[0041] S5. Based on the coupling relationship between the mechanical dynamic parameters and the thermodynamic parameters of the equipment, the parameter coupling correction of the equipment degradation feature subset is specifically implemented as follows: According to the correlation between the rotor speed and the vibration amplitude in the mechanical dynamic parameters of the equipment, the amplitude-frequency characteristics of the vibration amplitude in the equipment degradation feature subset are corrected. The mechanical dynamic parameters of the equipment include the rotor design speed and the critical speed. The critical speed is the rotor resonance critical speed disclosed in the equipment design parameters. For example, the critical speed of a certain model of steam turbine rotor is 3000rpm.

[0042] The relationship between rotor speed and vibration amplitude is determined by the resonance response law in the rotor dynamics equation. Specifically, when the actual operating speed is close to the critical speed, the vibration amplitude increases nonlinearly with the square of the ratio of the speed to the critical speed. The correction method is: calculate the vibration amplitude correction factor based on the ratio of the actual operating speed to the critical speed. For example, when the actual speed is 80% of the critical speed, the correction factor is 1 / (0.8 2 )=1.5625, multiply the original vibration amplitude in the equipment degradation feature subset by the correction factor to obtain the corrected vibration amplitude. For example, if the original vibration amplitude is 0.4mm / s², the corrected value is 0.4×1.5625=0.625mm / s², which reflects the actual vibration level when the rotor approaches the critical speed.

[0043] Based on the matching relationship between the thermal expansion coefficient and the temperature gradient in the thermodynamic parameters, the temperature gradient in the equipment degradation feature subset is corrected by thermal stress compensation. Thermodynamic parameters include the thermal expansion coefficient of the equipment material and the equipment structure size. The thermal expansion coefficient is the public physical parameter of the equipment material (such as alloy steel). For example, the thermal expansion coefficient of alloy steel is 12×10 -6 / ℃. The matching relationship between temperature gradient and thermal stress is determined by the thermal stress equation, which is: the thermal stress caused by temperature gradient is proportional to the product of thermal expansion coefficient, temperature change rate and equipment structure size. The correction method is: calculate the theoretical thermal strain according to the thermal expansion coefficient and equipment structure size. For example, if the equipment shaft length is 5 meters and the temperature gradient is 10℃ / h, the theoretical thermal strain is 12×10 -6 / ℃ ×5×10 3 mm ×10℃=0.6mm, if the actual measured thermal strain is 0.5mm, the compensation coefficient is 0.5 / 0.6≈0.833, and the original temperature gradient in the device degradation feature subset is multiplied by the compensation coefficient to obtain the corrected temperature gradient. For example, the original temperature gradient is 12℃ / h, and after correction it is 12×0.833≈10℃ / h to eliminate the measurement deviation caused by thermal expansion of the material.

[0044] The corrected vibration amplitude and temperature gradient are merged with the pressure gradient features to generate a parameter-coupled corrected equipment degradation feature subset. The merging method is: align the corrected vibration amplitude, temperature gradient and pressure gradient data according to the time window to form a multidimensional feature vector containing the corrected vibration amplitude, corrected temperature gradient and original pressure gradient. The length of the time window is consistent with the window length used when generating the operating condition feature set in step S2, such as a 10-minute window. The alignment method is: based on the sampling time point of the corrected vibration amplitude and temperature gradient, the pressure gradient data is aligned to the same time point by linear interpolation to ensure that all feature data are merged at the same timestamp. For example, if the corrected vibration amplitude in a certain time window is 0.625mm / s², the corrected temperature gradient is 10℃ / h, and the pressure gradient is 0.2MPa / s, then the merged feature vector is [0.625, 10, 0.2], which is output to the subsequent steps as the parameter-coupled corrected equipment degradation feature subset.

[0045] S6. Based on the corrected equipment degradation feature subset, trace back the potential fault source equipment along the equipment fault propagation map, determine the maintenance priority and generate maintenance instructions. The specific implementation is as follows: Obtain equipment fault propagation maps, which are constructed based on the mechanical connection relationship of the equipment and the energy transfer path, and include the fault propagation direction and weight between equipment nodes. The mechanical connection relationship of the equipment is determined according to the physical connection structure in the process pipeline and instrument flow chart of the equipment, such as the connection between the steam turbine and the generator through a coupling, the connection between the boiler feed pump and the pipeline through a flange, and the connection between the pipeline and the valve through a thread.

[0046] The energy transfer path is determined according to the flow direction of the medium, for example, steam flows from the boiler to the turbine, cooling water flows from the condenser to the cooling tower, and current flows from the generator to the transformer. The fault propagation weight is determined by analyzing the frequency statistics of the chain reaction of faults between device nodes in the historical fault records. For example, if the probability of a pump failure causing a valve failure in the historical data is 70%, the pump-to-valve propagation weight is set to 0.7; if the probability of a pipeline leak causing a pump failure is 50%, the pipeline-to-pump propagation weight is set to 0.5.

[0047] Abnormal parameters are extracted from the equipment degradation feature subset after parameter coupling correction, and the corresponding nodes in the equipment fault propagation graph are matched according to the abnormal parameter types.

[0048] The method for extracting abnormal parameters is: compare the vibration amplitude, temperature gradient and pressure gradient in the equipment degradation feature subset after parameter coupling correction with the preset normal range respectively. If a parameter exceeds the normal range, it is determined to be an abnormal parameter. The preset normal range is determined according to the statistical distribution of the equipment's historical operation data. For example, the normal range of vibration amplitude is the mean ± 3 times the standard deviation, the normal range of temperature gradient is 5℃ / h to 15℃ / h, and the normal range of pressure gradient is 0MPa / s to 0.3MPa / s.

[0049] The matching rules for matching the corresponding nodes in the equipment fault propagation map according to the abnormal parameter type are as follows: abnormal vibration amplitude is associated with rotating machinery nodes (such as turbine bearings and generator rotors), abnormal temperature gradient is associated with heat exchange equipment nodes (such as boiler pipes and condensers), and abnormal pressure gradient is associated with fluid control nodes (such as pumps and valves). For example, if the vibration amplitude of a certain time window in the equipment degradation feature subset after parameter coupling correction is 1.0mm / s² (exceeding the normal range of 0.8mm / s²), it is matched to the turbine bearing node; if the temperature gradient is 18℃ / h (exceeding the normal range of 15℃ / h), it is matched to the boiler pipe node.

[0050] Traverse in reverse along the fault propagation direction of the equipment fault propagation map to locate the potential fault source device node of the abnormal parameters. The specific method of reverse traversal is: starting from the device node where the abnormal parameters are detected, trace back layer by layer along the reverse path of the fault propagation direction until the root device without upstream nodes is found. For example, if the valve node detects an abnormal pressure gradient, the reverse traversal path is valve → pump → pipeline → boiler. If the historical fault weight of the pump node is 0.7 and there is an unrepaired record, the pump node is determined to be a potential fault source; if the pipeline node has no abnormalities and the boiler node is normal, the pump node is the final fault source. During the traversal process, the path with a high propagation weight is preferentially selected for backtracking. For example, the pump → valve path weight 0.7 is higher than the pipeline → pump path weight 0.5, so the pump → valve path is checked first.

[0051] Determine the maintenance priority based on the number of abnormal parameters and location depth of the potential fault source device node. The number of abnormal parameters is the total number of abnormal parameters of vibration amplitude, temperature gradient, and pressure gradient that appear simultaneously on the same device node. For example, if a pump node has abnormal vibration amplitude and temperature gradient, the number of abnormal parameters is 2. The location depth is the number of levels from the fault source node to the system entrance. For example, if the system entrance is a boiler, in the path of boiler→pipeline→pump→valve, the boiler depth is 1, the pipeline depth is 2, the pump depth is 3, and the valve depth is 4.

[0052] The maintenance priority calculation formula is: priority score = number of abnormal parameters × location depth coefficient. The location depth coefficient is set according to the number of levels. For example, the depth 1 coefficient is 1.0, the depth 2 coefficient is 1.2, the depth 3 coefficient is 1.5, and the depth 4 coefficient is 2.0. If the number of abnormal parameters of a pump node is 2 and the depth is 3, the priority score is 2×1.5=3.0.

[0053] Based on the maintenance priority and the remaining service life of the equipment, a maintenance instruction containing the fault source equipment identification and maintenance time is generated. The remaining service life of the equipment is estimated based on the ratio of the accumulated operation time of the equipment to the design life. For example, the design life of a pump is 10 years, it has been in operation for 8 years, and the remaining life is 2 years.

[0054] The rules for determining maintenance time are as follows: when the priority score is higher than the maintenance priority threshold and the remaining life is lower than the life threshold, emergency maintenance is triggered; when the priority score is higher than the threshold but the remaining life is higher than the threshold, planned maintenance is triggered. The maintenance priority threshold and life threshold are set according to the equipment operation and maintenance strategy. For example, the priority threshold is set to 2.0 and the life threshold is set to 1 year. If the priority score of a pump node is 3.0 and the remaining life is 0.8 years, an emergency maintenance instruction is generated and the maintenance time is set within 24 hours; if the remaining life is 1.5 years, a planned maintenance instruction is generated and the maintenance time is set within 7 days. The content of the maintenance instruction includes the fault source equipment identification (such as equipment number, installation location), abnormal parameter details (such as vibration amplitude 1.0mm / s²) and recommended maintenance measures (such as bearing replacement, seal inspection).

[0055] Embodiment 2: Figure 2 A structural schematic diagram of a data prediction system for thermal power plant equipment of the present invention is provided. The data prediction system for thermal power plant equipment comprises: Data acquisition and processing module: collects and pre-processes the operating status data of thermal power plant equipment; Working condition classification and extraction module: classifies the operating status data according to the preset load condition interval, extracts the time-frequency domain features within each load condition interval, and generates a working condition feature set; Dynamic strategy abnormality marking module: detects the switching frequency of the load condition interval. If the switching frequency exceeds the switching frequency threshold, the dynamic analysis strategy switching is initiated to mark the abnormal condition interval; Degradation feature decomposition module: Decomposes the operating condition feature set of the non-abnormal operating condition interval according to the similarity matching results between the historical degradation stage of the equipment and the current time-frequency domain features, and generates a subset of equipment degradation features; Parameter coupling correction module: Based on the coupling relationship between the mechanical dynamic parameters and thermodynamic parameters of the equipment, parameter coupling correction is performed on the equipment degradation feature subset; Fault backtracking decision module: Based on the corrected equipment degradation feature subset, it reversely traces back to the potential fault source equipment along the equipment fault propagation map, determines the maintenance priority and generates maintenance instructions.

[0056] It should be noted that the present invention can be deployed on the device itself to realize embedded applications, and can also be run on a PC or other terminal with a user interface, so as to meet various hardware environments and usage requirements.

[0057] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0058] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0059] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0060] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0061] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0062] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0063] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0064] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A data prediction method for thermal power plant equipment, characterized in that: The steps include: S1. Collect and pre-process the operating status data of thermal power plant equipment; S2. Classify the operating status data according to the preset load condition interval, extract the time-frequency domain features within each load condition interval, and generate a condition feature set; S3, detecting the switching frequency of the load condition interval, and if the switching frequency exceeds the switching frequency threshold, starting the dynamic analysis strategy switching to mark the abnormal condition interval; S4. Decomposing the operating condition feature set of the non-abnormal operating condition interval according to the similarity matching results between the historical degradation stage of the equipment and the current time-frequency domain features to generate an equipment degradation feature subset; S5. Based on the coupling relationship between the mechanical dynamic parameters and thermodynamic parameters of the equipment, parameter coupling correction is performed on the equipment degradation feature subset; S6. Based on the corrected equipment degradation feature subset, trace back the potential fault source equipment along the equipment fault propagation map, determine the maintenance priority and generate maintenance instructions.

2. The data prediction method for thermal power plant equipment according to claim 1, characterized in that: Collect and pre-process the operating status data of thermal power plant equipment, including: Synchronously collect vibration data, temperature data and pressure data of thermal power plant equipment, and align the timestamps of vibration data, temperature data and pressure data; Data cleaning is performed on the vibration data, temperature data and pressure data after time stamp alignment. Data cleaning includes using a sliding window filtering method to remove high-frequency noise components in the vibration data and removing abnormal outliers based on the correlation between temperature data and pressure data. The vibration data, temperature data and pressure data after cleaning are resampled according to the preset sampling frequency to generate a standardized operating status data set.

3. The data prediction method for thermal power plant equipment according to claim 1, characterized in that: The operating status data is classified according to the preset load condition interval, and the time-frequency domain features in each load condition interval are extracted to generate a condition feature set, including: The standardized operating status data set is divided into multiple load condition intervals according to the load value, and the load condition intervals include a low load interval, a medium load interval and a high load interval; Extract time-frequency domain features from vibration data, temperature data and pressure data in each load condition interval; The time-frequency domain features within the same load condition interval are merged according to the time window to generate a multi-dimensional condition feature set.

4. The data prediction method for thermal power plant equipment according to claim 3, characterized in that: The time-frequency domain characteristics of vibration data include vibration spectrum energy distribution, time-domain root mean square value and frequency-domain centroid frequency; the time-frequency domain characteristics of temperature data include temperature change rate and temperature fluctuation amplitude; the time-frequency domain characteristics of pressure data include pressure fluctuation frequency and pressure gradient.

5. The data prediction method for thermal power plant equipment according to claim 1, characterized in that: Detect the switching frequency of the load condition interval. If the switching frequency exceeds the switching frequency threshold, start the dynamic analysis strategy switching to mark the abnormal condition interval, including: The number of switching times in the load condition interval is counted based on the preset time window, the switching frequency per unit time is calculated, and it is determined whether the switching frequency exceeds the switching frequency threshold; If the switching frequency exceeds the switching frequency threshold, the time-frequency domain features of adjacent load condition intervals are aligned through the dynamic time warping method, and the cross-condition consistency index is extracted; If the cross-operating condition consistency index is lower than the consistency threshold, the load operating condition interval corresponding to the current time window is marked as an abnormal operating condition interval, and the re-extraction of the time-frequency domain features in the abnormal operating condition interval is triggered; otherwise, it is marked as a non-abnormal operating condition interval.

6. The data prediction method for thermal power plant equipment according to claim 5, characterized in that: Extracting the cross-operating condition consistency index includes: aligning the time-frequency domain features of adjacent load condition intervals, calculating the Pearson correlation coefficient for the aligned vibration spectrum energy distribution, temperature change rate and pressure gradient, and taking the average value of the Pearson correlation coefficient as the cross-operating condition consistency index.

7. The data prediction method for thermal power plant equipment according to claim 1, characterized in that: According to the similarity matching results between the historical degradation stage of the equipment and the current time-frequency domain features, the operating condition feature set of the non-abnormal operating condition interval is decomposed to generate a subset of equipment degradation features, including: For the current time-frequency domain features in the working condition feature set of the non-abnormal working condition interval, the matching degree between the current time-frequency domain features and the historical degradation stage of the equipment is determined according to the similarity calculation result between the historical degradation stage label of the equipment and the current time-frequency domain features; Among them, the equipment historical degradation stage label is divided by the vibration amplitude trend and temperature gradient trend in the equipment historical operation data; If the matching degree is greater than or equal to a preset matching threshold, a subset of equipment degradation features is extracted from the operating condition feature set of the non-abnormal operating condition interval using a low-frequency narrowband decomposition mode; If the matching degree is less than the preset matching threshold, based on the correlation between the harmonic component distribution of the vibration spectrum in the operating condition feature set of the non-abnormal operating condition interval and the critical speed of the rotor dynamic parameters, the frequency band where the harmonic energy is concentrated is selected for enhanced decomposition to generate a subset of equipment degradation features.

8. The data prediction method for thermal power plant equipment according to claim 1, characterized in that: Based on the coupling relationship between the mechanical dynamic parameters and thermodynamic parameters of the equipment, parameter coupling correction is performed on the equipment degradation feature subset, including: According to the correlation between the rotor speed and the vibration amplitude in the mechanical dynamics parameters of the equipment, the amplitude-frequency characteristics of the vibration amplitude in the equipment degradation feature subset are corrected; Based on the matching relationship between the thermal expansion coefficient and the temperature gradient in the thermodynamic parameters, thermal stress compensation correction is performed on the temperature gradient in the equipment degradation feature subset; The corrected vibration amplitude and temperature gradient are combined with the pressure gradient features to generate a parameter-coupled corrected device degradation feature subset.

9. The data prediction method for thermal power plant equipment according to claim 1, characterized in that: Based on the corrected equipment degradation feature subset, the potential fault source equipment is traced back along the equipment fault propagation map to determine the maintenance priority and generate maintenance instructions, including: Obtain equipment fault propagation maps, which are constructed based on the mechanical connection relationship and energy transfer path of the equipment, and include the fault propagation direction and weight between equipment nodes; Extract abnormal parameters from the equipment degradation feature subset after parameter coupling correction, and match the corresponding nodes in the equipment fault propagation graph according to the abnormal parameter type; Traverse in reverse along the fault propagation direction of the equipment fault propagation map to locate the potential fault source equipment nodes with abnormal parameters; Determine maintenance priority based on the number and location depth of abnormal parameters of potential fault source device nodes; Based on the maintenance priority and the remaining service life of the equipment, a maintenance instruction is generated, including the equipment identification of the fault source and the maintenance time.

10. A data prediction system for thermal power plant equipment, used to implement the data prediction method for thermal power plant equipment according to any one of claims 1 to 9, characterized in that: include: Data acquisition and processing module: collects and pre-processes the operating status data of thermal power plant equipment; Working condition classification and extraction module: classifies the operating status data according to the preset load condition interval, extracts the time-frequency domain features within each load condition interval, and generates a working condition feature set; Dynamic strategy abnormality marking module: detects the switching frequency of the load condition interval. If the switching frequency exceeds the switching frequency threshold, the dynamic analysis strategy switching is initiated to mark the abnormal condition interval; Degradation feature decomposition module: Decomposes the operating condition feature set of the non-abnormal operating condition interval according to the similarity matching results between the historical degradation stage of the equipment and the current time-frequency domain features, and generates a subset of equipment degradation features; Parameter coupling correction module: Based on the coupling relationship between the mechanical dynamic parameters and thermodynamic parameters of the equipment, parameter coupling correction is performed on the equipment degradation feature subset; Fault backtracking decision module: Based on the corrected equipment degradation feature subset, it reversely traces back to the potential fault source equipment along the equipment fault propagation map, determines the maintenance priority and generates maintenance instructions.

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