A new energy and environment-friendly equipment full life cycle intelligent maintenance early warning management method

Abstract

The application belongs to the technical field of new energy equipment operation and maintenance management, and specifically relates to a new energy environment-friendly equipment full-life-cycle intelligent maintenance early warning management method, which comprises the following steps: step 1: collecting multi-source heterogeneous data of new energy environment-friendly equipment; step 2: pre-processing and standardizing the multi-source heterogeneous data; step 3: based on the standardized multi-source data set, constructing a comprehensive representation index system of equipment health state; and step 4: based on the continuous change of the overall health index of the equipment on the time axis, generating a health evolution graph of the full-life-cycle of the equipment. The full-life-cycle health state is visualized and traceable: by constructing a comprehensive representation index system comprising three layers of structures, the full-life-cycle health state of the equipment from commissioning to decommissioning is quantitatively represented, and a health evolution graph is generated, thereby providing a unified data base and health state view for maintenance decision-making.

Description

Technical Field

[0001] This invention relates to the field of operation and maintenance management technology for new energy equipment, and in particular to an intelligent maintenance and early warning management method for the entire life cycle of new energy and environmental protection equipment. Background Technology

[0002] With the rapid development of the new energy industry, the installed capacity of new energy and environmental protection equipment such as wind power and photovoltaic power generation continues to expand, and the number of devices is growing exponentially. New energy and environmental protection equipment is typically deployed in remote areas (such as the Gobi Desert, deserts, coastlines, and mountains), operating in harsh environments. The equipment is diverse and dispersed, making traditional manual inspections and reactive maintenance methods insufficient to meet the operation and maintenance management needs of large-scale new energy power plants. How to shift from "passive maintenance" to "proactive maintenance" for new energy and environmental protection equipment and establish an intelligent maintenance and early warning system covering the entire equipment lifecycle has become a core issue that the industry urgently needs to address.

[0003] Currently, the operation and maintenance management of new energy and environmental protection equipment mainly relies on the following technical methods: First, traditional periodic inspections and planned maintenance, which involve inspecting and maintaining the equipment at fixed time intervals; second, fault alarm-driven reactive maintenance, which involves maintenance only after the equipment malfunctions or triggers a protection alarm; third, data acquisition and monitoring control systems deployed in some sites, which monitor equipment operating parameters in real time and trigger threshold alarms; and fourth, a few projects are attempting to use fault prediction models based on a single data source to assess the health status of specific components.

[0004] However, the aforementioned existing technologies have the following shortcomings in practical applications:

[0005] First, existing maintenance strategies lack a systematic perspective covering the entire lifecycle. Traditional planned maintenance and reactive repair models fail to incorporate the health status of equipment throughout its entire lifecycle, from commissioning, operation, degradation to retirement, into a unified management framework. Maintenance decisions are often based on a single assessment of the current state, lacking forward-looking predictions of equipment degradation trends and remaining lifespan. This leads to a coexistence of over-maintenance and under-maintenance, increasing operating costs while failing to effectively extend equipment lifespan.

[0006] Second, existing early warning methods rely on static thresholds or single signals, resulting in high false alarm and false negative rates. Traditional threshold alarm mechanisms, based on fixed upper and lower limit parameters, cannot adapt to normal parameter drift under different operating conditions, seasons, and aging stages of equipment, leading to a large number of invalid alarms interfering with normal operation and maintenance decisions. At the same time, single sensor signals are insufficient to comprehensively characterize the health status of equipment, and weak signals in the early stages of a fault are easily drowned out by noise, missing the golden window for early warning.

[0007] Third, there is insufficient integration and utilization of multi-source heterogeneous data, and a lack of cross-system collaborative analysis capabilities. New energy power plants deploy multiple subsystems such as production monitoring, auxiliary control, fire protection, and security, accumulating massive amounts of operational, environmental, and management data. However, this data is scattered across different systems, lacking effective integration and making it difficult to form a unified view of equipment health status. Existing technologies have failed to deeply integrate multi-dimensional information such as operational data, maintenance records, environmental parameters, and economic indicators, resulting in the underutilization of data value.

[0008] Fourth, existing maintenance decisions lack a mechanism for synergistic optimization of economic efficiency and safety. Current systems typically use technical indicators (such as vibration levels, temperature, and power deviation) as maintenance triggers, failing to incorporate economic factors such as the marginal benefits of improving equipment health, maintenance costs, and power generation losses into the decision-making framework. The formulation of maintenance measures lacks the precision of a "one-machine-one-policy" approach, making it difficult to achieve a balance between technical feasibility and economic rationality. Summary of the Invention

[0009] 1. Technical problems to be solved

[0010] The purpose of this invention is to solve the problems in the prior art by proposing a smart maintenance and early warning management method for the entire life cycle of new energy and environmental protection equipment that can integrate multi-source heterogeneous data, construct a health profile of the entire equipment life cycle, realize degradation trend prediction and intelligent early warning, and have the ability to optimize economic and safety collaborative maintenance decisions.

[0011] 2. Technical Solution

[0012] To achieve the above objectives, the present invention adopts the following technical solution:

[0013] Firstly, this application provides a method for intelligent maintenance and early warning management of new energy and environmental protection equipment throughout its entire life cycle, including the following steps:

[0014] Step 1: Collect multi-source heterogeneous data from new energy and environmental protection equipment. The multi-source heterogeneous data includes operating data, environmental data, maintenance data, and equipment static information.

[0015] Step 2: Preprocess and standardize the multi-source heterogeneous data to generate a standardized multi-source dataset;

[0016] Step 3: Based on the standardized multi-source dataset, construct a comprehensive indicator system for the health status of the equipment. The comprehensive indicator system includes a bottom indicator layer, a component health layer, and an overall equipment health index layer. Calculate the overall equipment health index.

[0017] Step 4: Based on the continuous changes of the overall health index of the equipment over time, generate a health evolution map of the equipment's entire life cycle.

[0018] One possible implementation also includes the steps of building a digital twin model and predicting degradation trends:

[0019] S1: Based on the static information and health evolution trajectory of the device, a digital twin model of the device is constructed by combining mechanism modeling and data-driven modeling;

[0020] S2: Compare the actual health index of the equipment with the theoretical health benchmark value predicted by the digital twin model, and calculate the health status deviation index;

[0021] S3: Based on the health status deviation index, a dual-channel prediction architecture is adopted to predict the equipment degradation trend. The dual-channel prediction architecture includes a Bayesian structure time series statistical prediction channel and a case matching prediction channel based on similarity retrieval.

[0022] S4: The outputs of the statistical prediction channel and the similarity prediction channel are weighted and fused to obtain the final degradation trend prediction result.

[0023] In one possible implementation, the weighted fusion weights of the dual-channel prediction architecture are dynamically adjusted based on the prediction confidence of the statistical prediction channel and the similarity prediction channel, respectively; the degradation trend prediction results include the predicted values ​​of the device health index for multiple future time steps and the corresponding prediction intervals.

[0024] One possible implementation also includes a step of triggering multi-level intelligent early warnings based on the degradation trend prediction results:

[0025] Based on the degradation trend prediction results and the current health status of the equipment, a three-level early warning triggering mechanism is executed:

[0026] When the predicted rate of decline of the health index exceeds the preset rate threshold within the first preset time period, a Level 1 warning is triggered.

[0027] When the health index is predicted to drop below the preset planned maintenance threshold within the second preset time period, a secondary planned maintenance warning is triggered, and a recommended maintenance time window is automatically generated.

[0028] When the health index falls below the preset emergency threshold or a failure is predicted to occur within the third preset time period in the future, a Level 3 emergency maintenance warning will be triggered.

[0029] The trigger thresholds for each level of early warning are dynamically calibrated based on historical equipment fault data and health evolution statistical distribution.

[0030] In one possible implementation, the overall health index of the equipment is calculated as follows: based on the original physical quantities and their derived statistical characteristics of the underlying indicator layer, the component health score of each key component is calculated; the health scores of each component are weighted and fused to obtain the overall health index of the equipment.

[0031] In one possible implementation, the health evolution map divides the equipment lifecycle into four stages: normal operation, slight decline, significant degradation, and high-risk failure; the health evolution map also includes a health evolution reference curve constructed based on historical data of similar equipment.

[0032] One possible implementation also includes steps for optimizing economic and safety collaborative maintenance decisions:

[0033] For each candidate maintenance measure, a comprehensive economic and safety evaluation index is constructed, which is composed of a weighted average of economic risk index and safety risk index.

[0034] Calculate the marginal benefit sensitivity of each candidate maintenance measure to the improvement of equipment health status, where the sensitivity is the partial derivative of the expected improvement in health index with respect to maintenance cost input;

[0035] Based on the aforementioned comprehensive economic and safety evaluation indicators and the marginal benefit sensitivity, a multi-objective optimization algorithm is used to solve for the optimal maintenance strategy.

[0036] In one possible implementation, the economic risk indicators include direct maintenance costs, power generation loss costs during maintenance, and post-maintenance benefits; the safety risk indicators include the probability of equipment failure before maintenance, the probability of failure and severity of consequences when maintenance is not performed, and the operational risk level during the implementation of maintenance measures; the multi-objective optimization algorithm uses an improved NSGA-II genetic algorithm to generate a Pareto optimal maintenance strategy set.

[0037] One possible implementation also includes maintenance closed-loop feedback and strategy iteration steps:

[0038] After the maintenance measures are implemented, the actual operating data of the equipment after maintenance are collected, the equipment health index is recalculated, and the health index after maintenance is compared and analyzed with the predicted value before maintenance to calculate the prediction deviation.

[0039] Based on the prediction bias, the degradation parameters in the digital twin model, the transition probability in the health prediction model, and the cost-benefit parameters in the sensitivity assessment are corrected online.

[0040] The corrected parameters will be used as the initial values ​​for the next round of maintenance strategy optimization iterations.

[0041] In one possible implementation, the preprocessing includes missing value imputation, outlier detection and labeling, and synchronous calibration of timestamps from multiple sources; the missing value imputation uses the KNN interpolation method based on similar operating conditions, and the outlier detection uses the 3σ criterion based on statistical distribution and the density-based local outlier factor method.

[0042] 3. Beneficial effects

[0043] Compared with the prior art, the advantages of this invention are:

[0044] (1) In this application, the visualization and traceability of the health status of the whole life cycle: by constructing a comprehensive characterization index system with a three-layer structure, the health status of the equipment from commissioning to decommissioning is quantitatively characterized, and a health evolution map is generated, providing a unified data foundation and health status view for maintenance decisions.

[0045] (2) In this application, the accurate degradation prediction of the hybrid driving model is achieved by using a digital twin architecture that combines mechanism modeling and data-driven modeling, integrating physical constraints and historical data to achieve accurate prediction of equipment degradation trends; the weighted fusion of the dual-channel prediction architecture effectively reduces the prediction bias of a single method and improves the accuracy and timeliness of early warning.

[0046] (3) In this application, the multi-level intelligent early warning and false alarm prevention mechanism: the dynamic threshold early warning system based on health index and degradation rate avoids the false alarm problem of traditional static threshold alarm; the hierarchical response strategy of the three-level early warning mechanism realizes the tiered early warning handling from attention to emergency, and improves the allocation efficiency of operation and maintenance resources.

[0047] (4) In this application, precise maintenance decision-making with economic and safety synergy: by constructing a comprehensive risk index of economic and safety and a marginal benefit sensitivity assessment, the quantitative assessment and priority ranking of maintenance measures are realized; the Pareto optimal strategy set is solved by multi-objective optimization, providing a unified solution of technical feasibility and economic rationality for operation and maintenance decision-making.

[0048] (5) In this application, closed-loop feedback and continuous optimization: the post-maintenance effect evaluation and parameter online correction mechanism form a closed-loop management of "prediction-decision-execution-feedback", which enables the maintenance strategy to be continuously optimized as the equipment status evolves and the environment changes, realizing a fundamental transformation from "passive maintenance" to "predictive maintenance". Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating an intelligent maintenance and early warning management method for the entire lifecycle of new energy and environmental protection equipment proposed in this invention.

[0050] Figure 2This is a schematic diagram of the process for constructing a digital twin model and predicting degradation trends proposed in this invention.

[0051] Figure 3 This is a schematic diagram of the process for triggering multi-level intelligent early warning based on the degradation trend prediction results proposed in this invention. Detailed Implementation

[0052] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0053] Reference Figure 1-3 A method for intelligent maintenance and early warning management of new energy and environmental protection equipment throughout its entire life cycle includes the following steps:

[0054] Step 1: Collect multi-source heterogeneous data from new energy and environmental protection equipment. The multi-source heterogeneous data includes operation data, environmental data, maintenance data, and equipment static information.

[0055] Step 2: Preprocess and standardize the multi-source heterogeneous data to generate a standardized multi-source dataset; in this embodiment, the preprocessing includes missing value imputation, outlier detection and labeling, and synchronization calibration of multi-source data timestamps; the missing value imputation adopts the KNN interpolation method based on similar working conditions, and the outlier detection adopts the 3σ criterion based on statistical distribution and the density-based local outlier factor square.

[0056] Step 3: Based on the standardized multi-source dataset, construct a comprehensive indicator system for the health status of the equipment. The comprehensive indicator system includes a bottom indicator layer, a component health layer, and an overall equipment health index layer. Calculate the overall equipment health index.

[0057] Step 4: Based on the continuous changes of the overall health index of the equipment over time, generate a health evolution map of the equipment's entire life cycle.

[0058] In this embodiment of the application, the method further includes the step of constructing a digital twin model and predicting degradation trends:

[0059] S1: Based on the static information and health evolution trajectory of the device, a digital twin model of the device is constructed by combining mechanism modeling and data-driven modeling;

[0060] S2: Compare the actual health index of the equipment with the theoretical health benchmark value predicted by the digital twin model, and calculate the health status deviation index;

[0061] S3: Based on the health status deviation index, a dual-channel prediction architecture is adopted to predict the equipment degradation trend. The dual-channel prediction architecture includes a Bayesian structure time series statistical prediction channel and a case matching prediction channel based on similarity retrieval.

[0062] S4: The outputs of the statistical prediction channel and the similarity prediction channel are weighted and fused to obtain the final degradation trend prediction result.

[0063] In this embodiment of the application, the weighted fusion weight of the dual-channel prediction architecture is dynamically adjusted according to the prediction confidence of the statistical prediction channel and the similarity prediction channel respectively; the degradation trend prediction result includes the predicted values ​​of the device health index for multiple future time steps and the corresponding prediction intervals.

[0064] In this embodiment of the application, the method further includes the step of triggering multi-level intelligent early warning based on the degradation trend prediction results:

[0065] Based on the degradation trend prediction results and the current health status of the equipment, a three-level early warning triggering mechanism is executed:

[0066] When the predicted rate of decline of the health index exceeds the preset rate threshold within the first preset time period, a Level 1 warning is triggered.

[0067] When the health index is predicted to drop below the preset planned maintenance threshold within the second preset time period, a secondary planned maintenance warning is triggered, and a recommended maintenance time window is automatically generated.

[0068] When the health index falls below the preset emergency threshold or a failure is predicted to occur within the third preset time period in the future, a Level 3 emergency maintenance warning will be triggered.

[0069] The trigger thresholds for each level of early warning are dynamically calibrated based on historical equipment fault data and health evolution statistical distribution.

[0070] In this embodiment of the application, the overall health index of the equipment is calculated as follows: based on the original physical quantities and their derived statistical characteristics of the underlying index layer, the component health score of each key component is calculated; the component health scores are weighted and fused to obtain the overall health index of the equipment.

[0071] In this embodiment of the application, the health evolution map divides the equipment life cycle into four stages: normal operation period, slight decline period, significant degradation period, and high-risk failure period; the health evolution map also includes a health evolution reference baseline curve constructed based on historical data of similar equipment.

[0072] In this embodiment of the application, the step of optimizing economic-safety collaborative maintenance decisions is also included:

[0073] For each candidate maintenance measure, a comprehensive economic and safety evaluation index is constructed, which is composed of a weighted average of economic risk index and safety risk index.

[0074] Calculate the marginal benefit sensitivity of each candidate maintenance measure to the improvement of equipment health status, where the sensitivity is the partial derivative of the expected improvement in health index with respect to maintenance cost input;

[0075] Based on the aforementioned comprehensive economic and safety evaluation indicators and the marginal benefit sensitivity, a multi-objective optimization algorithm is used to solve for the optimal maintenance strategy.

[0076] In this embodiment, the economic risk indicators include direct maintenance costs, power generation loss costs during maintenance, and post-maintenance benefits; the safety risk indicators include the probability of equipment failure before maintenance, the probability of failure and severity of consequences when no maintenance is performed, and the operational risk level during the implementation of maintenance measures; the multi-objective optimization algorithm uses an improved NSGA-II genetic algorithm to generate a Pareto optimal maintenance strategy set.

[0077] In this embodiment of the application, maintenance closed-loop feedback and strategy iteration steps are also included:

[0078] After the maintenance measures are implemented, the actual operating data of the equipment after maintenance are collected, the equipment health index is recalculated, and the health index after maintenance is compared and analyzed with the predicted value before maintenance to calculate the prediction deviation.

[0079] Based on the prediction bias, the degradation parameters in the digital twin model, the transition probability in the health prediction model, and the cost-benefit parameters in the sensitivity assessment are corrected online.

[0080] The corrected parameters will be used as the initial values ​​for the next round of maintenance strategy optimization iterations.

[0081] Example 1:

[0082] This embodiment provides a method for constructing a full life-cycle health profile of new energy and environmental protection equipment, which constructs a multi-dimensional quantitative representation of the equipment's health status by integrating multi-source heterogeneous data.

[0083] Step 1: Collect multi-source heterogeneous data from new energy and environmental protection equipment

[0084] Multi-source heterogeneous data for new energy and environmental protection equipment includes:

[0085] Operating data includes, but is not limited to, real-time operating parameters of the equipment such as voltage, current, power, speed, temperature, vibration, and pressure, which are collected through the equipment's own sensors or SCADA system;

[0086] Environmental data includes, but is not limited to, environmental parameters such as wind speed, wind direction, irradiance, ambient temperature, humidity, and dust concentration, which are collected by environmental monitoring sensors deployed around the equipment.

[0087] Maintenance and repair data includes, but is not limited to, historical equipment failure records, repair work orders, component replacement records, and inspection results, which are entered into the site operation and maintenance management system.

[0088] Static equipment information includes, but is not limited to, equipment model, manufacturing date, commissioning date, design life, and key component parameters, which are obtained from the equipment asset ledger.

[0089] Step 2: Preprocess and standardize the multi-source heterogeneous data.

[0090] Preprocessing of the collected multi-source heterogeneous data includes:

[0091] Missing value handling: Use linear interpolation or KNN imputation method based on similar working conditions to fill in missing data;

[0092] Outlier detection: Employing a statistical distribution-based 3D model. Criteria and density-based local outlier factor methods are used to identify and label outlier data;

[0093] Time synchronization calibration: unify the timestamps of different data sources to ensure data consistency in the time dimension.

[0094] The preprocessed data is standardized according to a unified data format to generate a standardized multi-source dataset.

[0095] Step 3: Construct a comprehensive indicator system to represent the health status of equipment.

[0096] Based on the standardized multi-source dataset, a comprehensive indicator system for representing the health status of equipment is constructed. This comprehensive indicator system comprises a three-layer structure:

[0097] The first layer is the bottom index layer, which consists of the raw physical quantities collected by each sensor and their derived statistical features, including but not limited to time-domain statistics such as mean, standard deviation, rate of change, peak value, and kurtosis, as well as frequency-domain features such as FFT spectral features and wavelet packet energy features.

[0098] The second layer is the component health layer. For each key component of the equipment (such as blades, gearboxes, generators, and converters in wind turbines, and inverters, combiner boxes, and photovoltaic modules in photovoltaic equipment), a weighted aggregation based on the underlying indicators is used to obtain a health score for each component.

[0099]

[0100] in, For the first Health rating of each component For the first The first component A fundamental indicator, For the corresponding index normalization function, Indicator weights For the first The number of indicators for each component;

[0101] The third layer is the overall equipment health index layer, which weights and merges the health scores of each component to obtain the overall equipment health index:

[0102]

[0103] in, For the overall health index of the equipment, For the number of key components, For the first The weighting coefficient of each component .

[0104] Step 4: Generate a health evolution map of the entire device lifecycle.

[0105] Based on the overall health index of the equipment The continuous changes over time construct the health evolution trajectory of the equipment from commissioning to the present. This health evolution trajectory is divided into four stages: normal operation period, slight decline period, significant degradation period, and high-risk failure period, forming a health status stage label for the entire life cycle of the equipment.

[0106] Meanwhile, based on the health evolution data of similar historical equipment, a similarity matching method is used to construct a reference baseline curve for equipment health evolution, providing a reference for subsequent degradation trend prediction.

[0107] Example 2:

[0108] This embodiment provides a method for predicting and intelligently warning the degradation trend of new energy and environmental protection equipment based on digital twins and hybrid driving models.

[0109] Step 1: Build a digital twin model of the device.

[0110] Based on the device's static information and health evolution trajectory, a digital twin model of the device is constructed using a combination of mechanistic modeling and data-driven modeling.

[0111] The mechanistic modeling part constructs a theoretical behavioral model of the equipment based on its physical equations (such as the aerodynamic equations of a wind turbine and the dynamic equations of its transmission chain). The data-driven modeling part uses long short-term memory networks or gated recurrent units to learn the degradation patterns of the equipment from historical operating data.

[0112] The digital twin model takes the current equipment operating parameters and environmental parameters as input and outputs the theoretical health status benchmark value of the equipment under the current operating conditions.

[0113] Step 2: Calculate the health status deviation index

[0114] The actual health index of the equipment Theoretical health baseline predicted by digital twin model Compare and calculate the health status deviation index:

[0115]

[0116] Simultaneously, the first derivative of the health status deviation index over time is calculated. It is used to assess the rate of degradation of the health status of equipment.

[0117] Step 3: Predicting the trend of fusion degradation

[0118] A dual-channel prediction architecture is used to predict device degradation trends:

[0119] First channel: Based on the historical sequence of the health status deviation index, a Bayesian structured time series model is used to extrapolate the degradation trend and output the predicted value and prediction range of the equipment health index at the next T time points;

[0120] The second channel: Based on the similarity matching between the device health evolution trajectory and the reference baseline curve, a similarity retrieval method is used to find the K historical cases most similar to the current device degradation mode from the historical database. The subsequent degradation trajectories of these cases are used as a reference to generate similarity prediction results of degradation trends.

[0121] The statistical prediction results of the first channel and the similarity prediction results of the second channel are weighted and fused to obtain the final degradation trend prediction result:

[0122]

[0123] in, The combined predicted health index value. These are the predicted values ​​from the statistical model. For similarity prediction values, To integrate the weighting coefficients, the values ​​are dynamically adjusted based on the prediction confidence of the two methods.

[0124] Step 4: Trigger multi-level intelligent early warning

[0125] Based on the degradation trend prediction results and the current health status of the equipment, multi-level intelligent early warning triggering is executed:

[0126] Level 1 Warning (Attention Warning): When the predicted rate of decline in the health index within the next T1 time steps exceeds the preset rate threshold. If triggered by a time event, it is recommended to increase the monitoring frequency.

[0127] Level 2 Warning (Planned Maintenance Warning): When it is predicted that the health index will drop to the preset planned maintenance threshold within the next T2 time steps. The following events will trigger an automatic generation of a recommended maintenance time window;

[0128] Level 3 Warning (Emergency Maintenance Warning): When the health index falls below the preset emergency threshold. If the fault is predicted to occur within the next T3 time steps, it is recommended to arrange maintenance immediately.

[0129] The trigger thresholds for each level of warning , , Dynamic calibration is performed based on the equipment's historical fault data and health evolution statistical distribution, and adaptive adjustments are made as the equipment's life cycle stages change.

[0130] Example 3:

[0131] This embodiment provides an intelligent maintenance decision optimization method based on comprehensive economic and safety risk indicators.

[0132] Step 1: Construct a comprehensive economic and safety evaluation index for maintenance measures

[0133] For each candidate maintenance measure Construct a comprehensive economic and security assessment index :

[0134]

[0135] Among them, economic risk indicators Indicators representing the economic efficiency and safety risks of maintenance measures Characterize the safety benefits of maintenance measures. The weighting coefficients are for economic and security considerations.

[0136] Economic risk indicators It consists of the following sub-items:

[0137] Direct maintenance costs include spare parts costs, labor costs, and equipment rental costs.

[0138] Power generation loss cost: Power generation loss caused by equipment downtime during maintenance;

[0139] Post-maintenance benefits: Increased power generation revenue resulting from the restoration of equipment performance after maintenance and reduced depreciation and amortization due to extended equipment lifespan.

[0140] Safety risk indicators It consists of the following sub-items:

[0141] Equipment failure probability before maintenance (assessed based on the degradation trend prediction results);

[0142] Without maintenance, the probability of failure and the severity of consequences during future maintenance cycles;

[0143] The level of operational risk during the implementation of maintenance measures.

[0144] Step 2: Assess the marginal benefit sensitivity of maintenance measures

[0145] For each candidate maintenance measure Calculate its sensitivity to improvements in equipment health status:

[0146]

[0147] in, To determine the expected improvement in the equipment health index after maintenance, The total cost of maintenance measures.

[0148] The sensitivity value It reflects the marginal benefit of unit maintenance cost investment in improving equipment health status and is the core basis for prioritizing maintenance measures.

[0149] Step 3: Perform multi-objective optimization and solve for the optimal maintenance strategy.

[0150] With minimizing the total lifecycle risk cost as the objective function, and with maintenance budget constraints, available maintenance resource constraints, and maintenance time window constraints as constraints, a multi-objective optimization problem is constructed:

[0151]

[0152] in, The time discount factor, This refers to the planned maintenance cycle length.

[0153] The optimization problem is solved using an improved NSGA-II multi-objective genetic algorithm to generate a Pareto optimal maintenance strategy set, where each strategy in the Pareto optimal maintenance strategy set corresponds to a different economic-safety trade-off preference.

[0154] Step 4: Implement closed-loop feedback and strategy iteration for maintenance.

[0155] After the maintenance measures are implemented, the actual operating data of the equipment after maintenance are collected, the equipment health index is recalculated, and the health index after maintenance is compared and analyzed with the predicted value before maintenance to calculate the prediction deviation.

[0156] Based on the prediction deviation, the following parameters are corrected online:

[0157] Update the degradation parameters in the digital twin model;

[0158] Correcting the transition probabilities in health prediction models;

[0159] Adjust the cost-benefit parameters in the sensitivity assessment;

[0160] The corrected parameters are used as the initial values ​​for the next round of maintenance strategy optimization iterations, forming a closed-loop continuous optimization mechanism for maintenance decisions.

[0161] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for intelligent maintenance and early warning management of new energy and environmental protection equipment throughout its entire life cycle, characterized in that, Includes the following steps: Step 1: Collect multi-source heterogeneous data from new energy and environmental protection equipment. The multi-source heterogeneous data includes operating data, environmental data, maintenance data, and equipment static information. Step 2: Preprocess and standardize the multi-source heterogeneous data to generate a standardized multi-source dataset; Step 3: Based on the standardized multi-source dataset, construct a comprehensive indicator system for the health status of the equipment. The comprehensive indicator system includes a bottom indicator layer, a component health layer, and an overall equipment health index layer. Calculate the overall equipment health index. Step 4: Based on the continuous changes of the overall health index of the equipment over time, generate a health evolution map of the equipment's entire life cycle.

2. The intelligent maintenance and early warning management method for the entire life cycle of new energy and environmental protection equipment according to claim 1, characterized in that, It also includes the steps of building a digital twin model and predicting degradation trends: S1: Based on the static information and health evolution trajectory of the device, a digital twin model of the device is constructed by combining mechanism modeling and data-driven modeling; S2: Compare the actual health index of the equipment with the theoretical health benchmark value predicted by the digital twin model, and calculate the health status deviation index; S3: Based on the health status deviation index, a dual-channel prediction architecture is adopted to predict the equipment degradation trend. The dual-channel prediction architecture includes a Bayesian structure time series statistical prediction channel and a case matching prediction channel based on similarity retrieval. S4: The outputs of the statistical prediction channel and the similarity prediction channel are weighted and fused to obtain the final degradation trend prediction result.

3. The intelligent maintenance and early warning management method for the entire life cycle of new energy and environmental protection equipment according to claim 2, characterized in that, The weighted fusion weights of the dual-channel prediction architecture are dynamically adjusted based on the prediction confidence of the statistical prediction channel and the similarity prediction channel, respectively; the degradation trend prediction results include the predicted values ​​of the device health index for multiple future time steps and the corresponding prediction intervals.

4. The intelligent maintenance and early warning management method for the entire life cycle of new energy and environmental protection equipment according to claim 2, characterized in that, It also includes the step of triggering multi-level intelligent early warnings based on the degradation trend prediction results: Based on the degradation trend prediction results and the current health status of the equipment, a three-level early warning triggering mechanism is executed: When the predicted rate of decline of the health index exceeds the preset rate threshold within the first preset time period, a Level 1 warning is triggered. When the health index is predicted to drop below the preset planned maintenance threshold within the second preset time period, a secondary planned maintenance warning is triggered, and a recommended maintenance time window is automatically generated. When the health index falls below the preset emergency threshold or a failure is predicted to occur within the third preset time period in the future, a Level 3 emergency maintenance warning will be triggered. The trigger thresholds for each level of early warning are dynamically calibrated based on historical equipment fault data and health evolution statistical distribution.

5. The intelligent maintenance and early warning management method for the entire life cycle of new energy and environmental protection equipment according to claim 1, characterized in that, The overall health index of the equipment is calculated as follows: based on the original physical quantities and their derived statistical characteristics of the underlying indicator layer, the component health score of each key component is calculated; the health scores of each component are weighted and fused to obtain the overall health index of the equipment.

6. The intelligent maintenance and early warning management method for the entire life cycle of new energy and environmental protection equipment according to claim 1, characterized in that, The health evolution map divides the equipment lifecycle into four stages: normal operation, slight decline, significant degradation, and high-risk failure. The health evolution map also includes a health evolution reference curve constructed based on historical data of similar equipment.

7. The intelligent maintenance and early warning management method for the entire life cycle of new energy and environmental protection equipment according to claim 1, characterized in that, It also includes steps for optimizing economic and safety collaborative maintenance decisions: For each candidate maintenance measure, a comprehensive economic and safety evaluation index is constructed, which is composed of a weighted average of economic risk index and safety risk index. Calculate the marginal benefit sensitivity of each candidate maintenance measure to the improvement of equipment health status, where the sensitivity is the partial derivative of the expected improvement in health index with respect to maintenance cost input; Based on the aforementioned comprehensive economic and safety evaluation indicators and the marginal benefit sensitivity, a multi-objective optimization algorithm is used to solve for the optimal maintenance strategy.

8. The intelligent maintenance and early warning management method for the entire life cycle of new energy and environmental protection equipment according to claim 7, characterized in that, The economic risk indicators include direct maintenance costs, power generation loss costs during maintenance, and post-maintenance benefits; the safety risk indicators include the probability of equipment failure before maintenance, the probability of failure and severity of consequences when maintenance is not performed, and the operational risk level during the implementation of maintenance measures; the multi-objective optimization algorithm uses an improved NSGA-II genetic algorithm to generate a Pareto optimal maintenance strategy set.

9. A method for intelligent maintenance and early warning management of new energy and environmental protection equipment throughout its entire life cycle, as described in claim 7, is characterized in that... It also includes maintenance closed-loop feedback and strategy iteration steps: After the maintenance measures are implemented, the actual operating data of the equipment after maintenance are collected, the equipment health index is recalculated, and the health index after maintenance is compared and analyzed with the predicted value before maintenance to calculate the prediction deviation. Based on the prediction bias, the degradation parameters in the digital twin model, the transition probability in the health prediction model, and the cost-benefit parameters in the sensitivity assessment are corrected online. The corrected parameters will be used as the initial values ​​for the next round of maintenance strategy optimization iterations.

10. The intelligent maintenance and early warning management method for the entire life cycle of new energy and environmental protection equipment according to claim 1, characterized in that, The preprocessing includes missing value imputation, outlier detection and labeling, and synchronous calibration of timestamps from multiple sources. The missing value imputation adopts the KNN interpolation method based on similar working conditions, and the outlier detection adopts the 3σ criterion based on statistical distribution and the density-based local outlier factor method.

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