Unit variable working condition multi-element fusion trend early warning method and system

Abstract

The application provides a unit variable working condition multi-element fusion trend early warning method and system, belongs to the technical field of power generation equipment state monitoring and predictive maintenance, and can at least partially solve the problems of high false negative rate, poor time-varying adaptability of equipment characteristics, inability to quantify the influence of maintenance events, and distortion of cross-working condition benchmarking in the prior art early warning technology, and comprises the following steps: collecting unit historical operation data to construct a unified data base; constructing a time-varying characteristic model based on the unified data base to generate a time-varying benchmark characteristic curve; constructing an operation and maintenance event knowledge base, quantitatively evaluating the performance step recovery amount before and after the event and the degradation rate correction coefficient based on the knowledge base, and correcting the time-varying benchmark characteristic curve; constructing a multi-dimensional working condition vector and retrieving a similar health working condition segment, calculating the performance offset degree and performing trend prediction, and triggering early warning when the predicted value exceeds a preset threshold; calculating the early warning confidence and generating a structured early warning report. The application accurately captures the early degradation trend of the equipment performance and provides a reliable basis for unit maintenance.

Description

Technical Field

[0001] This invention belongs to the field of power generation equipment condition monitoring and predictive maintenance technology, specifically involving a method and system for early warning of multi-factor integrated trends in unit operating conditions. Background Technology

[0002] Combined cycle (CCPP) gas turbine units, as the main power source for grid peak shaving, operate under variable conditions of cold / warm start, rapid load increases / decreases, and frequent start-stops. Accurate detection and early warning of early performance degradation trends are crucial for achieving condition-based maintenance, reducing operating costs, and avoiding unplanned downtime. Existing early warning technologies are designed around steady-state operating conditions and, despite multiple optimizations, still cannot adapt to the characteristics of variable operating conditions. The core shortcomings are as follows: Poor adaptability to changing operating conditions and high false alarm rate in dynamic processes: Technicians have tried to optimize traditional early warning methods by setting floating thresholds and increasing moving average windows, but they still cannot distinguish between drastic fluctuations in parameters in dynamic processes and real signs of degradation. The false alarm rate in dynamic operating conditions is as high as 40% or more, and maintenance personnel are often forced to block alarms, missing the opportunity to detect early faults.

[0003] The disconnect between time-varying equipment patterns and event impact handling: While some existing technologies can track performance degradation, their online time-series window-based matching methods cannot distinguish between normal aging and performance jumps caused by maintenance, leading to distortion of the baseline curve after maintenance. Other technologies require model retraining after major maintenance, disrupting the historical consistency of trend analysis. This invention solves the problems of characterizing time-varying patterns and quantifying the impact of maintenance simultaneously through non-real-time event-driven baseline curve self-calibration.

[0004] The impact of maintenance events cannot be quantified, and baseline consistency is disrupted: technicians have not established a response mechanism for maintenance events, the performance step recovery caused by events such as overhaul / water washing / component replacement cannot be quantified, data before and after maintenance cannot be connected, baseline status is difficult to determine, historical consistency of trend analysis is disrupted, and the distortion rate of subsequent early warning results exceeds 50%.

[0005] Cross-timescale benchmarking is difficult and results are distorted: Technicians use a simple “comparison of the same load point” for trend analysis, ignoring changes in boundary conditions such as ambient temperature, humidity, and fuel calorific value, making it difficult to find truly similar operating points. The distortion rate of the benchmarking results exceeds 40%, making it impossible to achieve an effective comparison with the “health status of the same period in history”.

[0006] To address this, we propose a method and system for early warning of the trend of multi-factor integration of unit operating conditions. Summary of the Invention

[0007] The present invention aims to solve at least one of the technical problems existing in the prior art, and to provide a method and system for early warning of multi-factor integrated trends of unit operating conditions.

[0008] One aspect of the present invention provides a multi-dimensional integrated trend early warning method for unit operating conditions, comprising the following steps: S1: Collecting historical operating data of the unit, and after data preprocessing and dynamic labeling based on load change rate, constructing a unified data foundation integrating operation and event information; S2: Based on the unified data foundation, constructing a time-varying characteristic model integrating physical mechanism and data-driven approach for the target equipment, and characterizing the natural aging law of the unit equipment by generating a time-varying reference characteristic curve; S3: Constructing a knowledge base of operation and maintenance events, and quantitatively evaluating the performance step recovery amount and degradation rate correction coefficient before and after the event based on the knowledge base of operation and maintenance events. S4: Based on the performance step recovery amount and the degradation rate correction coefficient, the time-varying baseline characteristic curve is shifted and trend-corrected to obtain an updated time-varying baseline characteristic curve; S5: A multi-dimensional operating condition vector is constructed, and similar healthy operating condition segments are retrieved. The performance deviation between the current performance index and the historical similar operating condition health value is calculated. The performance deviation is trend-predicted using a time-series model. When the predicted value exceeds the dynamic warning threshold, an early warning is triggered and the remaining available time is predicted; S6: The warning confidence is calculated by combining the multi-dimensional features of the time-varying characteristic model, operating condition matching, and trend prediction, and a structured warning report is generated.

[0009] Further, in step S1, the data preprocessing includes data cleaning and hard synchronization, wherein the data cleaning includes: based on 3 Outlier detection and removal using criteria and the isolated forest algorithm, short-term missing data completion based on linear interpolation, and labeling and cause recording of long-term missing data; the hard synchronization includes: achieving time synchronization of all data through a hardware clock.

[0010] Furthermore, in step S2, the target equipment includes: a compressor, a combustion chamber, a turbine, a waste heat boiler, and a steam turbine.

[0011] Further, in step S2, the time-varying reference characteristic curve Calculate using the following formula: ; in: As a degradation factor, Based on the efficiency; The degradation factor Calculate using the following formula: ; in: For equipment uptime, This refers to the number of times the equipment has been started and stopped. For equivalent full-hours, , The fitting coefficients are denoted as .

[0012] Furthermore, in step S3, the maintenance and repair event knowledge base includes the mapping relationship between maintenance or repair event types, affected equipment, performance indicators, and expected effects; the performance step recovery amount is the difference between the statistical average values ​​of performance indicators of steady-state data for 72 hours before and after the maintenance or repair event; and the degradation rate correction coefficient is the ratio of degradation rates before and after the maintenance or repair event.

[0013] Furthermore, in step S3, the triggering conditions for the translation correction include: unplanned performance step events, i.e., the performance step of the equipment exceeds a preset threshold; the triggering conditions for the trend correction include: planned maintenance events, i.e., planned major repairs, planned minor repairs, and replacement of key components.

[0014] Further, in step S4, the performance offset Calculate as follows: ; in: The current value, This is the historical average. This represents the historical standard deviation.

[0015] Further, in step S5, the warning confidence level Calculate as follows: ; in: The prediction interval width coefficient of the time-varying characteristic model, The similarity coefficient for the working conditions. is the stability coefficient for the trend prediction.

[0016] Another aspect of the present invention provides a multi-factor integrated trend early warning system for unit variable operating conditions, used to implement the multi-factor integrated trend early warning method for unit variable operating conditions described above. The system includes: a historical operating data base module for constructing a unified data base integrating operating and event information; a time-varying equipment analysis and modeling module for constructing a time-varying characteristic model integrating physical mechanisms and data-driven approaches, generating a time-varying baseline characteristic curve; and a maintenance event response module for constructing a maintenance event knowledge base, and quantitatively evaluating the performance step recovery before and after a maintenance or repair event based on the maintenance event knowledge base. The system includes a degradation rate correction coefficient; a baseline characteristic self-calibration module, used to perform translation and trend correction on the time-varying baseline characteristic curve based on the performance step recovery amount and the degradation rate correction coefficient; a cross-period benchmarking trend early warning module, used to construct a multi-dimensional operating condition vector and retrieve similar healthy operating condition segments, calculate the performance offset and perform trend prediction through a time series model, and trigger an early warning when the predicted value exceeds a preset threshold; and an early warning confidence assessment module, used to calculate the early warning confidence level by integrating the multi-dimensional features of the time-varying characteristic model, operating condition matching and trend prediction, and generate a structured early warning report.

[0017] Furthermore, the hardware carriers of the system are a data storage server, a modeling and analysis server, and an early warning display terminal; the software carriers of the system are a data cleaning program, a time-varying modeling program, a working condition matching program, and a time series prediction program, and each module achieves data interaction through an industrial communication protocol.

[0018] The beneficial effects of this invention are as follows: The accuracy of variable operating condition early warning has been greatly improved: By using a non-real-time event-driven baseline curve self-correction mechanism and multi-dimensional operating condition similarity retrieval, the "dynamic failure" problem that online time-series matching is susceptible to transient fluctuations is avoided. It also overcomes the limitation that the operating condition gridded model cannot adapt to the step changes in equipment performance, reducing the false alarm rate of variable operating conditions from more than 40% to less than 5%, and the overall prediction and early warning accuracy rate reaches more than 96%.

[0019] Accurate tracking of equipment time-varying patterns: Time-varying reference characteristic curves depict the natural aging of equipment, reducing the proportion of normal aging being misjudged as abnormal from over 30% to below 8%, thus avoiding ineffective operation and maintenance.

[0020] The impact of maintenance events is quantified, and the consistency of the baseline is guaranteed: the baseline curve is adaptively corrected after maintenance to eliminate the baseline deviation. The distortion rate of subsequent early warning results is reduced from more than 50% to less than 10%, ensuring the historical consistency of trend analysis.

[0021] Accurate cross-timescale benchmarking: Multi-dimensional operating condition similarity matching overcomes the influence of boundary conditions, reducing the distortion rate of benchmarking results from over 40% to below 10%, achieving effective comparison with the health status of the same period in history.

[0022] It provides strong early warning guidance and supports condition-based maintenance: it outputs quantitative performance deviation, remaining available time and confidence level, and provides possible causes based on similar historical cases, providing a scientific and practical basis for condition-based maintenance of units. The number of unplanned maintenance operations of units is reduced by more than 70%, and maintenance costs are reduced by more than 30%. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating the steps of the unit variable operating condition multi-factor integrated trend early warning method according to an embodiment of the present invention; Figure 2 This is an overall flowchart of the unit variable operating condition multi-factor fusion trend early warning method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of the unit variable operating condition multi-element fusion trend early warning system according to an embodiment of the present invention; Figure 4 This is a comparison chart of the compressor time-varying reference characteristic curve before and after translational correction and trend correction under non-real-time water washing event triggering, according to an embodiment of the present invention. Figure 5 This is a schematic diagram illustrating the health condition segment retrieval and performance deviation trend warning based on multi-dimensional operating condition vectors in an embodiment of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0025] Please refer to the following as well. Figure 1 and Figure 2The unit variable operating condition multi-dimensional integrated trend early warning method provided by the specific embodiments of the present invention includes the following steps: S1: Collect historical operating data of the unit, and after data preprocessing and dynamic labeling based on load change rate, construct a unified data foundation integrating operation and event information; S2: Based on the unified data foundation, construct a time-varying characteristic model integrating physical mechanism and data-driven model for the target equipment, and characterize the natural aging law of the unit equipment by generating time-varying reference characteristic curves; S3: Construct an operation and maintenance event knowledge base, and quantitatively evaluate the performance step recovery amount and degradation rate before and after the event based on the operation and maintenance event knowledge base. S4: Construct a multi-dimensional operating condition vector and retrieve similar healthy operating condition segments. Calculate the performance deviation between the current performance index and the historical similar operating condition health value. Perform trend prediction on the performance deviation using a time-series model. When the predicted value exceeds the dynamic warning threshold, trigger an early warning and predict the remaining available time. S5: Calculate the warning confidence level by integrating the multi-dimensional features of the time-varying characteristic model, operating condition matching, and trend prediction, and generate a structured warning report.

[0026] In this embodiment, in step S1, the data preprocessing includes data cleaning and hard synchronization. The data cleaning includes: based on 3D... Outlier detection and removal using criteria and the isolated forest algorithm, short-term missing data completion based on linear interpolation, and labeling and cause recording of long-term missing data; the hard synchronization includes: achieving time synchronization of all data through a hardware clock.

[0027] The historical operating data of the unit specifically includes: DCS (Distributed Control System) and DEH (Digital Electro-hydraulic Control System) process data, TSI (Turbine Safety Monitoring System) vibration data, and online performance calculation data; it also covers a full record of maintenance events originating from the Maintenance and Management System (EAM). These maintenance event records include operations such as major overhauls, minor repairs, water washing, and component replacements, and record key fields such as event type, occurrence time, and detailed description.

[0028] Specifically, in step S1, the step of dividing the steady-state and dynamic states of the sampling point data based on the load change rate includes: using the load change rate as the criterion, dividing the data into steady-state and dynamic states, and labeling each sampling point with a "steady-state / dynamic" label.

[0029] In this embodiment, in step S2, the target equipment includes: a compressor, a combustion chamber, a turbine, a waste heat boiler, and a steam turbine.

[0030] In this embodiment, in step S2, the time-varying reference characteristic curve Calculate using the following formula: ; in: As a degradation factor, Based on the efficiency; The degradation factor Calculate using the following formula: ; in: For equipment uptime, This refers to the number of times the equipment has been started and stopped. For equivalent full-hours, , The fitting coefficients are denoted as .

[0031] Specifically, this embodiment uses a compressor as an example to illustrate the principle, constructing a mechanistic relationship between pressure ratio, efficiency, equivalent speed, and flow rate based on general characteristic curves. Furthermore, a degradation factor that evolves over time is introduced. Increase equipment efficiency Expressed as ;(in Driven by running time, number of start-stop cycles, and equivalent full-load hours. Based on nonlinear regression or Gaussian process regression algorithms, the degradation model... ; Perform parameter identification and fitting to determine the optimal fitting coefficients. , Furthermore, a "time-varying reference characteristic curve" of the equipment is generated to accurately depict the natural aging process of the equipment.

[0032] In this embodiment, in step S3, the maintenance and repair event knowledge base includes the mapping relationship between maintenance or repair event types, affected equipment, performance indicators, and expected effects; the performance step recovery amount is the difference between the statistical average values ​​of performance indicators of steady-state data for 72 hours before and after the maintenance or repair event; and the degradation rate correction coefficient is the ratio of degradation rates before and after the maintenance or repair event.

[0033] Specifically, the construction process of the maintenance event knowledge base includes: First, classifying and coding events such as major overhauls, minor repairs, compressor water washing, burner replacement, and filter replacement, and establishing a mapping relationship of "event type - affected equipment - performance index - expected effect" (e.g., compressor water washing - compressor - efficiency / pressure ratio - efficiency recovery of 0.5%~1.5%). A non-real-time event triggering mechanism is adopted, automatically triggering the event response analysis algorithm only when a planned maintenance event or an unplanned performance step event is detected: extracting stable operating data for 72 hours before and after the event, calculating the statistical average of performance indicators under the same operating conditions to obtain the performance step recovery amount; and comparing the degradation rate before and after the event to calculate the degradation rate correction coefficient, thereby providing data support for the online correction of the time-varying reference characteristic curve, that is: performing a translation correction on the time-varying reference characteristic curve to match the current health status of the equipment and trend correction to adapt to the new degradation rate after the event, to obtain an updated event reference characteristic curve.

[0034] In this embodiment, in step S3, the triggering conditions for translation correction include: unplanned performance step events, i.e., the performance step of the equipment exceeds a preset threshold; the triggering conditions for trend correction include: planned maintenance events, i.e., planned major repairs, planned minor repairs, and replacement of key components.

[0035] Specifically, the translation correction step includes: performing translation correction on the time-varying reference characteristic curve based on the performance step recovery amount to achieve overall curve translation to match the current health status of the equipment; the trend correction step includes: performing trend correction on the curve based on the degradation rate correction coefficient to eliminate the reference deviation caused by the planned maintenance event by adjusting the curve slope to adapt to the new degradation rate after the event occurs.

[0036] In this embodiment, in step S4, the performance offset Calculate as follows: ; in: The current value, This is the historical average. This represents the historical standard deviation.

[0037] Specifically, the elements of the constructed multidimensional operating condition vector include: load, ambient temperature, atmospheric pressure, relative humidity, IGV opening degree, and fuel calorific value. In other embodiments, the multidimensional operating condition vector may also include other elements. Then, using a weighted Euclidean distance algorithm or a dynamic time warping (DTW) algorithm, the K most similar healthy operating condition segments to the current operating condition are retrieved from the historical data base; the performance deviation between the current performance index and the historical similar operating condition health values ​​is calculated. ;in, The current value, This is the historical average. For historical standard deviation; Trend analysis was performed on the time series data. Short-term degradation rate was calculated using an adaptive sliding window, and the LSTM / Transformer model was used to predict medium- and long-term degradation. Value; when predicted Values ​​exceeding dynamic warning thresholds (e.g.: >3, indicating an orange alert. When the value is >4 (a red alert), an early warning is triggered, and the remaining available time of the device in the current state is calculated.

[0038] In this embodiment, in step S5, the warning confidence level Calculate as follows: ; in: The prediction interval width coefficient of the time-varying characteristic model, The similarity coefficient for the working conditions. is the stability coefficient for the trend prediction.

[0039] Specifically, in step S5, the warning confidence level It is calculated based on a weighted average of three dimensions, namely ;in, This is the prediction interval width coefficient of the time-varying characteristic model, which is negatively correlated with the prediction interval width. The similarity coefficient for the aforementioned working conditions is positively correlated with the similarity. The stability coefficient for trend prediction is negatively correlated with the prediction variance. Subsequently, a structured early warning report is generated, including warning time, equipment, indicators, current offset, risk level, trend prediction, remaining available time, confidence level, possible causes, and similar historical cases. The offset trend curve and reasoning are presented through a visual interface.

[0040] Please refer to the following: Figure 3Another aspect of the present invention provides a multi-factor integrated trend early warning system for unit variable operating conditions, used to implement the multi-factor integrated trend early warning method for unit variable operating conditions described above. The system includes: a historical operating data base module 31, used to construct a unified data base integrating operating and event information; a time-varying equipment analysis and modeling module 32, used to construct a time-varying characteristic model integrating physical mechanisms and data-driven approaches, generating a time-varying baseline characteristic curve; and a maintenance event response module 33, used to construct a maintenance event knowledge base, and based on the maintenance event knowledge base, quantitatively evaluate the performance step recovery amount before and after a maintenance or repair event. The system includes a degradation rate correction coefficient; a baseline characteristic self-calibration module 34, used to perform translation and trend correction on the time-varying baseline characteristic curve based on the performance step recovery amount and the degradation rate correction coefficient; a cross-period benchmarking trend early warning module 35, used to construct a multi-dimensional operating condition vector and retrieve similar healthy operating condition segments, calculate the performance offset and perform trend prediction through a time series model, and trigger an early warning when the predicted value exceeds a preset threshold; and an early warning confidence assessment module 36, used to calculate the early warning confidence level by integrating the multi-dimensional features of the time-varying characteristic model, operating condition matching and trend prediction, and generate a structured early warning report.

[0041] In this embodiment, the hardware carriers of the system are a data storage server, a modeling and analysis server, and an early warning display terminal; the software carriers of the system are a data cleaning program, a time-varying modeling program, a working condition matching program, and a time series prediction program. Each module interacts with the other via an industrial communication protocol. Specifically, the historical operating data base module 31, the equipment time-varying analysis and modeling module 32, the maintenance event response module 33, the benchmark characteristic self-calibration module 34, the inter-period benchmarking trend early warning module 35, and the early warning confidence assessment module 36 are sequentially connected bidirectionally.

[0042] The historical operation data base module 31 not only acquires process data from DCS (Distributed Control System) and DEH (Digital Electro-hydraulic Control System), vibration data from TSI (Turbine Safety Monitoring System), and online performance calculation data, but also acquires a full record of operation and maintenance events from the EAM maintenance management system 37 of the external system. The operation and maintenance event records include operations such as overhaul, minor repair, water washing, and component replacement, and record key fields such as event type, occurrence time, and detailed description.

[0043] When the EAM maintenance management system 37 of the external system detects an event, it triggers the maintenance event response module 33 to perform response analysis and calculation: extracting stable operating data for 72 hours before and after the event, calculating the statistical average of performance indicators under the same operating conditions to obtain the performance step recovery amount; and comparing the degradation rate before and after the event to calculate the degradation rate correction coefficient, thereby providing data support for the online correction of the time-varying reference characteristic curve.

[0044] The warning confidence module 36 calculates and obtains the warning confidence level. Subsequently, a structured early warning report is generated, which includes the early warning time, equipment, indicators, current offset, risk level, trend prediction, remaining available time, confidence level, possible causes, and similar historical cases. This report is then output to the early warning display terminal 38 to visualize the offset trend curve and reasoning basis.

[0045] Please refer to the following as well. Figure 4 and Figure 5 Based on the actual early warning scenarios of the compressor of the 9F-class CCPP unit, the specific implementation of the present invention is described in detail. Those skilled in the art can carry out engineering implementation based on this description and specific power plant configurations.

[0046] Specifically, the system hardware and software are first deployed, as follows: The hardware carrier of the system includes a historical operation data storage server, a modeling and analysis server (equipped with an LSTM / Transformer model), and a 4K early warning display terminal, all connected via an industrial Ethernet network; the software carrier includes 3 The isolated forest data cleaning program, nonlinear regression time-varying modeling program, DTW condition matching program, time series prediction program, and early warning report generation program are all deployed on the modeling and analysis server.

[0047] Step 1: Collect DCS, vibration, performance data, and maintenance events (including two compressor water washes and one minor overhaul) from the unit's five years of operation. +Isolated forest algorithm for cleaning, timestamp alignment and "steady state / dynamic" labeling, building a unified data foundation.

[0048] Step 2: Construct a time-varying characteristic model of the compressor and obtain the fitting result. ; Generate a time-varying baseline characteristic curve of compressor efficiency to characterize its natural aging rate of 0.8% per year.

[0049] Step 3: The modeling and analysis server continuously monitors the event logs of the external EAM maintenance management system. When a non-real-time planned maintenance event, "compressor water washing," is detected, the maintenance event response module is automatically triggered. This module extracts 72 hours of steady-state data before and after the event, calculating an efficiency performance step recovery of 1.2% and a degradation rate correction coefficient of 0.75. Subsequently, the baseline characteristic self-calibration module performs the following: first, a translation correction is performed, shifting the time-varying baseline characteristic curve upwards by 1.2% to match the health state after water washing; then, a trend correction is performed, adjusting the curve slope (i.e., degradation rate) to 0.75 times its original value to accommodate the slower degradation rate after water washing. The entire process does not require model retraining or continuous time window matching; instead, it is a one-time, precise calibration based on a clearly triggered event.

[0050] Step 4: The inter-period benchmarking trend early warning module constructs a six-dimensional operating condition vector including load, ambient temperature, atmospheric pressure, relative humidity, IGV opening, and fuel calorific value. The DTW algorithm is used to retrieve the 10 health condition segments most similar to the current operating condition from a 5-year historical data base, and the performance deviation of the current compressor efficiency relative to these health condition segments is calculated. =2.8 (Yellow Alert); LSTM Model Prediction With 0.02 The daily increase is expected to reach 3.0 in 7 days (orange alert).

[0051] Step 5: Calculate the early warning confidence level The system generates an early warning report, indicating that "early deterioration of compressor efficiency may be caused by increased pressure differential at the inlet filter or initial scaling on the blades. It is recommended to check the filter and arrange for borehole probing." The report also displays the offset trend curve and a similar case to that in 2023.

[0052] The implementation effects of this specific implementation method are as follows: After the 9F-class CCPP unit was deployed and operated for 4 months, the false alarm rate of the variable operating condition warning was reduced to 3%, the overall warning accuracy rate reached 97%, the distortion rate of the warning results after maintenance was reduced to 8%, the number of unplanned maintenance of the unit was reduced by 75%, and the operation and maintenance cost was reduced by 35%. It successfully achieved accurate capture of the early deterioration trend of equipment performance and provided a reliable basis for condition-based maintenance.

[0053] In summary, the embodiments disclosed herein have at least the following technical effects: The accuracy of variable operating condition early warning has been greatly improved: it covers all operating conditions and has strong adaptability to dynamic operating conditions. The false alarm rate of variable operating conditions has been reduced from more than 40% to less than 5%, and the overall prediction and early warning accuracy rate has reached more than 96%, solving the problem of "dynamic failure".

[0054] Accurate tracking of equipment time-varying patterns: Time-varying reference characteristic curves depict the natural aging of equipment, reducing the proportion of normal aging being misjudged as abnormal from over 30% to below 8%, thus avoiding ineffective operation and maintenance.

[0055] The impact of maintenance events is quantified, and the consistency of the baseline is guaranteed: the baseline curve is adaptively corrected after maintenance to eliminate the baseline deviation. The distortion rate of subsequent early warning results is reduced from more than 50% to less than 10%, ensuring the historical consistency of trend analysis.

[0056] Accurate cross-timescale benchmarking: Multi-dimensional operating condition similarity matching overcomes the influence of boundary conditions, reducing the distortion rate of benchmarking results from over 40% to below 10%, achieving effective comparison with the health status of the same period in history.

[0057] It provides strong early warning guidance and supports condition-based maintenance: it outputs quantitative performance deviation, remaining available time and confidence level, and provides possible causes based on similar historical cases, providing a scientific and practical basis for condition-based maintenance of units. The number of unplanned maintenance operations of units is reduced by more than 70%, and maintenance costs are reduced by more than 30%.

[0058] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of the present invention, and the present invention is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A method for early warning of trends in multi-factor integration of unit operating conditions, characterized in that, Includes the following steps: S1: Collect historical operating data of the unit, and after data preprocessing and dynamic labeling based on load change rate, construct a unified data foundation that integrates operation and event information; S2: Based on the unified data foundation, a time-varying characteristic model integrating physical mechanisms and data-driven approaches is constructed for the target equipment. By generating time-varying reference characteristic curves, the natural aging law of the unit equipment is characterized. S3: Construct an operation and maintenance event knowledge base, and based on the operation and maintenance event knowledge base, quantitatively evaluate the performance step recovery amount and degradation rate correction coefficient before and after the event. Based on the performance step recovery amount and the degradation rate correction coefficient, perform translation correction and trend correction on the time-varying reference characteristic curve to obtain the updated time-varying reference characteristic curve. S4: Construct a multi-dimensional operating condition vector and retrieve similar healthy operating condition segments. Calculate the performance deviation between the current performance index and the historical similar operating condition health value. Perform trend prediction on the performance deviation through a time series model. When the predicted value exceeds the dynamic early warning threshold, trigger an early warning and predict the remaining available time. S5: Calculate the early warning confidence level by combining the multi-dimensional features of the time-varying characteristic model, operating condition matching, and trend prediction, and generate a structured early warning report.

2. The unit variable operating condition multi-factor integrated trend early warning method according to claim 1, characterized in that, In step S1, the data preprocessing includes data cleaning and hard synchronization. The data cleaning includes: based on 3 Outlier detection and removal using criteria and the isolated forest algorithm, short-term missing data completion based on linear interpolation, and labeling and cause recording of long-term missing data; The hard synchronization includes: synchronizing all data time through a hardware clock.

3. The unit variable operating condition multi-factor integrated trend early warning method according to claim 1, characterized in that, In step S2, the target equipment includes: a compressor, a combustion chamber, a turbine, a waste heat boiler, and a steam turbine.

4. The unit variable operating condition multi-factor integrated trend early warning method according to claim 1, characterized in that, In step S2, The time-varying reference characteristic curve Calculate using the following formula: ； in: As a degradation factor, Based on the efficiency; The degradation factor Calculate using the following formula: ； in: For equipment uptime, This refers to the number of times the equipment has been started and stopped. For equivalent full-hours, , represents the fitting coefficient.

5. The unit variable operating condition multi-factor integrated trend early warning method according to claim 1, characterized in that, In step S3, The operation and maintenance event knowledge base includes the mapping relationship between operation and maintenance or maintenance event types, affected equipment, performance indicators, and expected results; The performance step recovery amount is the difference between the statistical average values ​​of the performance indicators of the steady-state data for 72 hours before and after the operation and maintenance or repair event; The degradation rate correction factor is the ratio of the degradation rate before and after an operation or maintenance event.

6. The unit variable operating condition multi-factor integrated trend early warning method according to claim 1, characterized in that, In step S3, The triggering conditions for the translation correction include: an unplanned performance step event, that is, the performance step of the device exceeds a preset threshold. The triggering conditions for the trend correction include: planned maintenance events, namely planned major repairs, planned minor repairs, and replacement of critical components.

7. The unit variable operating condition multi-factor integrated trend early warning method according to claim 1, characterized in that, In step S4, the performance offset Calculate as follows: ； in: The current value, This is the historical average. The standard deviation is the historical value.

8. The unit variable operating condition multi-factor integrated trend early warning method according to claim 1, characterized in that, In step S5, the warning confidence level Calculate as follows: ； in: The prediction interval width coefficient of the time-varying characteristic model, The similarity coefficient for the working conditions. is the stability coefficient for the trend prediction.

9. A multi-faceted trend early warning system for unit variable operating conditions, characterized in that: For implementing the unit variable operating condition multi-factor integrated trend early warning method according to any one of claims 1 to 8, the system comprises: The historical operation data base module is used to build a unified data base that integrates operation and event information; The equipment time-varying analysis and modeling module is used to construct a time-varying characteristic model that integrates physical mechanisms and data-driven approaches, and generate time-varying baseline characteristic curves. The maintenance event response module is used to build a knowledge base for operation and maintenance events, and to quantitatively evaluate the performance step recovery amount and degradation rate correction coefficient before and after the operation or maintenance event based on the knowledge base for operation and maintenance events. The reference characteristic self-calibration module is used to perform translation and trend correction on the time-varying reference characteristic curve based on the performance step recovery amount and the degradation rate correction coefficient. The inter-period benchmarking trend early warning module is used to construct a multi-dimensional operating condition vector and retrieve similar healthy operating condition segments, calculate the performance deviation and make trend predictions through a time series model, and trigger an early warning when the predicted value exceeds a preset threshold. The early warning confidence assessment module is used to calculate the early warning confidence level by integrating the multi-dimensional features of the time-varying characteristic model, operating condition matching, and trend prediction, and to generate a structured early warning report.

10. The unit variable operating condition multi-factor integrated trend early warning system according to claim 9, characterized in that, The hardware carriers of the system are a data storage server, a modeling and analysis server, and an early warning display terminal; The system's software components include a data cleaning program, a time-varying modeling program, a working condition matching program, and a time series prediction program. Each module interacts with the other through an industrial communication protocol.

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