A maintenance-oriented motor generator full life cycle cost performance evaluation system

Abstract

The application provides a kind of motor generator full life cycle cost performance evaluation system for overhaul.The system includes sequentially connected equipment data acquisition module, cost performance calculation module, overhaul decision evaluation module and display and interactive module; through the interface of equipment data acquisition module, the data acquisition and extraction of bottom terminal equipment, the cost performance calculation module calculates the change of overhaul cost input and operation performance according to equipment operation data, obtains the cost performance relationship result uploaded to the overhaul decision evaluation module to analyze the single overhaul operation and maintenance benefit and the optimization effect of equipment full life cycle management, finally sets up the display and interactive module to informationally display the evaluation decision result and interactively optimizes with actual operation and maintenance management.The application realizes the management optimization of equipment operation and maintenance work, reduces the cost performance ratio in the full life cycle of equipment, realizes the comprehensive optimization of economy and reliability of equipment management.

Description

Technical Field

[0001] This invention relates to the field of power equipment lifecycle management, and in particular to a lifecycle cost-efficiency assessment system for electric generators oriented towards maintenance. Background Technology

[0002] Under the requirements of the high-density equipment scale and refined management model of the new power system, electric generators are a key object of power enterprise asset management. Strengthening the utilization and management of equipment operation data and effectively controlling various economic cost inputs and performance returns are important aspects of equipment management. The traditional fixed mode of periodic pre-testing and planned maintenance of electric generators does not meet the requirements of comprehensive optimization of equipment management economy and reliability.

[0003] In existing technologies, the patent "A Cost-Benefit Analysis Method for Distributed Renewable Energy Generation Operation Modes" determines the optimal operation mode by identifying the revenue / cost ratio of distributed generation under different operation modes throughout its entire life cycle and selecting the scheme with the highest value. However, for electric generators, considering only the cost-efficiency ratio for maintenance decisions does not meet the comprehensive requirements of equipment reliability and economy. Therefore, it is necessary to comprehensively and accurately evaluate maintenance decisions from multiple factors, including cost-efficiency ratio, cost input, efficiency improvement, and risk assessment, in order to formulate scientific and reasonable maintenance decisions. This is of great significance for improving the economic benefits of power plants and enhancing the stability of power systems. Summary of the Invention

[0004] The purpose of this invention is to improve the traditional power asset management model and provide a method and system for evaluating the cost-effectiveness of electric generators throughout their entire life cycle, oriented towards maintenance. This invention collects data records from the financial system, operational ticketing system, and SCADA system throughout the entire life cycle of the electric generator. Using the collected and processed data, the cost-effectiveness calculation module performs calculations, and the resulting calculations are transmitted to the maintenance decision evaluation module. After refinement and interactive processing, the results, including maintenance decisions, maintenance costs, and efficiency optimization, are displayed in the interactive display module.

[0005] The objective of this invention is achieved by at least one of the following technical solutions.

[0006] A life-cycle cost-efficiency assessment system for electric generators for maintenance includes a sequentially connected equipment data acquisition module, cost-efficiency calculation module, maintenance decision assessment module, and display and interaction module.

[0007] The equipment data acquisition module is equipped with a data acquisition interface to collect and extract data records from the financial system, operation ticket system, and SCADA system throughout the equipment's entire lifecycle. This enables real-time monitoring of equipment operation and cost data, which is then transmitted to the cost-efficiency calculation module.

[0008] The cost-effectiveness calculation module calculates the cost input and operational efficiency changes of a single maintenance operation based on the received equipment operation and cost data. It comprehensively represents the cost and efficiency through the cost-effectiveness ratio. Based on the maintenance cost input, efficiency changes, and cost-effectiveness ratio of the maintenance plan obtained from the cost-effectiveness calculation module, and taking into account operational risks, the module evaluates the maintenance decision.

[0009] The maintenance decision evaluation module is used to calculate the resource allocation of the maintenance plan based on the equipment cost-efficiency calculation results, conduct an economic evaluation of the equipment's entire life cycle, optimize the management plan based on the full life cycle records, and transmit the optimized plan and evaluation results to the display and interaction module.

[0010] The display and interaction module is used to obtain the full life cycle cost and performance evaluation plan of electric generators, to visualize the operation and maintenance logs, condition-based maintenance predictions and preventive maintenance plan evaluations, and to set up interfaces to provide human-computer interaction functions, providing information-based guidance for real-time operation and maintenance management.

[0011] Furthermore, the equipment data acquisition module is equipped with hardware sensors and data interfaces that are associated with financial statistics, two-ticket records, and SCADA equipment operation data. It uses intelligent text recognition to collect and aggregate data based on a unified communication networking protocol. The data is classified and normalized through a data platform and mapped to the device tree. The data is then exported as a unified CSV file format for easy access and analysis by the calculation module.

[0012] Furthermore, in the cost-efficiency calculation module, based on equipment operation and cost data, the module calculates the maintenance cost input and operational efficiency changes involving different types of maintenance content, and comprehensively represents the cost and efficiency situation through cost-efficiency ratio, and transmits the maintenance cost input, efficiency changes and cost-efficiency ratio calculation results to the maintenance decision evaluation module.

[0013] The cost-effectiveness ratio is the ratio of maintenance costs to operational efficiency of an electric generator at the same point in time.

[0014] Furthermore, in the cost-efficiency calculation module, a full life-cycle cost estimation model for electric generators is established: combining industry implementation cases of full life-cycle cost estimation for electric generators and cost parameter characteristics, targeted modeling is performed for each cost, including investment cost (CI), operating cost (CO), maintenance cost (CM), failure cost (CF), and disposal cost (CD); the full life-cycle cost (LCC) includes:

[0015] LCC = CI + CO + CM + CF + CD;

[0016] The calculation of maintenance costs is based on historical data statistics and uses a heuristic prediction algorithm to predict trends. As the historical cost records increase, the correction parameters of the heuristic prediction algorithm are iteratively updated, and the maintenance cost prediction results are updated in each iteration.

[0017] Furthermore, in the cost-efficiency calculation module, an equipment architecture model of the electric generator system is established; the electric generator unit system is defined to include: the electric generator body (stator, rotor and its electrical parts), air cooling system, upper and lower guides and thrust bearings, braking and stopping system, and auxiliary systems such as electric heaters and fire extinguishing systems;

[0018] The calculation of operational efficiency changes is based on the ADC model method, establishing the availability matrix A and the reliability matrix D of the electric generator system; establishing the inherent capability index system of the electric generator, using the analytic hierarchy process and entropy weight method to determine the weight of each index in the inherent capability system, and calculating the inherent capability matrix C;

[0019] This paper defines the availability states of electric generators and their auxiliary equipment, as well as the probability of state transitions between these states. Using the Markov state transition method, it predicts and calculates emergency, major, and general faults of the electric generators and their auxiliary equipment. The results are then applied to determine preventative maintenance (condition-based maintenance). Based on the performance indicators for grid-connected operation of the electric generators, a comprehensive evaluation of the prediction results is conducted using subjective and objective evaluation methods. Maintenance decisions are then made based on the evaluation results. Finally, using the ADC model, the paper obtains the real-time performance changes of the electric generators and their auxiliary equipment before and after implementing the formulated maintenance decisions throughout their entire lifecycle.

[0020] Furthermore, the calculation of the availability matrix A is as follows:

[0021] Regarding the operating states of an electric generator system, which are categorized into three states: normal operation, non-stop fault, and fault state, represented by numbers a1, a2, and a3 respectively; the availability vector of the electric generator system can be expressed as: A = [a1, a2, a3], where a1 is the number of available states and a2 is the number of available states. i The calculations are based on the functional architecture coordination relationship and reliability theory of electric generators, i = 1, 2, 3;

[0022] The credibility matrix D is as follows:

[0023] Credibility is defined as d ij This indicates that the turbine generator system is in state a. i Change to state a jThe probability; based on the constraints calculated by the credibility, the state transition matrix of the electric generator satisfies d ij =0, i>j, that is, the state transition during the execution of the switching task is a one-way, one-time change, and conforms to the randomness of Markov state transition; therefore, the failure rate λ of the electric generator system is used to reflect the probability of the event of state change during the process, and the confidence matrix D is obtained by using the Laplace transform method.

[0024] The inherent performance indicators of electric generators include the assessment indicators of each functional component of the electric generator and its auxiliary equipment. Based on the power system equipment performance evaluation standards, the performance characteristics of electric generators are evaluated, including output ramp-up speed and transient steady-state characteristics.

[0025] The inherent capability matrix C is calculated using the fuzzy comprehensive evaluation method. The specific process is as follows: establish an evaluation level set V based on the set of evaluation factors; calculate subjective weights using the AHP method and objective weights using the entropy weight method, combine the weights, and establish a weight set W; generate a membership degree matrix R for each element in the corresponding evaluation level set V according to the membership degree of the evaluation indicators; and obtain the inherent capability matrix C by solving the equation C = W * R * V.

[0026] Based on the availability, reliability, and inherent capability module results obtained from the ADC model, the comprehensive evaluation result E of equipment performance is defined, and the performance value E of the electric generator system is obtained by solving the equation E = A × D × C.

[0027] Furthermore, considering the impact of maintenance on electric generators, based on historical operation and maintenance data, the method of returning to service life is used to quantify the change in equipment failure rate before and after maintenance, and the credibility D matrix is ​​corrected to obtain the post-maintenance performance value, thereby realizing a closed-loop interaction between maintenance decision-making and the evaluation of maintenance decisions.

[0028] Furthermore, in the maintenance decision-making module, based on the maintenance cost input and efficiency value change results obtained from the cost-efficiency calculation module, a cost-efficiency coupled analysis is performed on the electric generator equipment system. The benefits of a single maintenance decision scheme are evaluated according to the optimal cost-efficiency ratio method. At the whole life cycle scale, maintenance cost input, efficiency improvement, cost-efficiency ratio and operational risks are considered simultaneously. Maintenance decisions are made by balancing economy and reliability. Based on historical data and service life rollback methods, the cost-efficiency ratio benefits of maintenance decisions are predicted to guide the optimization of maintenance decisions.

[0029] Furthermore, the cost-effectiveness calculation module outputs results to the maintenance decision module for formulating various maintenance decisions. Based on these decisions, the service life rollback method is used to predict the failure rate of the electric generator after maintenance. The prediction results are then output to the cost-effectiveness calculation module, which calculates the cost-effectiveness ratio of various maintenance decisions. The calculation results are then output to the maintenance decision module, which selects the optimal maintenance decision from among the various options. That is, it selects the maintenance decision scheme with the lowest cost-effectiveness ratio while ensuring the safe and reliable operation of the equipment. The optimal maintenance decision is then output to the display and interaction module.

[0030] Furthermore, in the display and interaction module, a human-computer interaction module is set up based on the electric generator operation log, the maintenance prediction output by the maintenance decision evaluation module, and the maintenance evaluation results. By displaying cost curves, efficiency curves, and cost-effectiveness curves, the operation and maintenance benefits of the electric generator throughout its entire life cycle are presented intuitively, providing digital and visual interactive functions for operation and maintenance management personnel to carry out maintenance business.

[0031] Compared with the prior art, the advantages of the present invention are as follows:

[0032] Compared to the conventional approach of solely relying on cost-effectiveness ratios to determine solutions, this invention comprehensively considers cost input, efficiency improvement, cost-effectiveness ratio, and operational risks, balancing economic efficiency and reliability to formulate maintenance decisions. Furthermore, based on historical data and service life rollback methods, it predicts the cost-effectiveness benefits of maintenance decisions, guiding optimization of these decisions. Finally, it establishes a comprehensive system through progressively layered modules, from underlying data analysis to decision presentation. Attached Figure Description

[0033] Figure 1 An architecture diagram of a maintenance-oriented electric generator life-cycle cost-effectiveness assessment system in this invention embodiment.

[0034] Figure 2 A complete flowchart of the cost-effective method in this embodiment of the invention. Detailed Implementation

[0035] The present invention has been described in detail above through embodiments. These embodiments do not constitute a limitation on the scope of the invention. All technical solutions or method improvements made by those skilled in the art based on the technical solutions described in this invention, and within the scope and protection of this invention, are protected by this patent.

[0036] Example:

[0037] A life-cycle cost-efficiency assessment system for electric generators for maintenance includes a sequentially connected equipment data acquisition module, cost-efficiency calculation module, maintenance decision assessment module, and display and interaction module.

[0038] The equipment data acquisition module is equipped with a data acquisition interface to collect and extract data records from the financial system, the two-ticket system, and the SCADA system throughout the entire life cycle of the equipment. This enables real-time monitoring of equipment operation and cost data, and the data is then cleaned and processed before being transmitted to the cost-efficiency calculation module.

[0039] The cost-effectiveness calculation module calculates the cost input and operational efficiency changes of a single maintenance operation based on the received equipment operation and cost data. It comprehensively represents the cost and efficiency situation through the cost-effectiveness ratio, taking into account the cost input and efficiency improvement. The cost-effectiveness ratio calculation results are used to evaluate the resource allocation and life-cycle economy of the maintenance plan, and output the three calculation results to the maintenance decision evaluation module.

[0040] In the cost-effectiveness calculation module, intelligent identification of cost information, correction fitting, and gray wolf optimization heuristic algorithm are used to collect cost data to achieve cost aggregation;

[0041] The variable cost model is as follows:

[0042]

[0043] In this embodiment, SC represents the variable cost, including operating cost, maintenance cost, and failure cost; k is an empirical coefficient; and η... energy P represents the unit's power loss rate. N The rated capacity of the unit is 300MW, T is the average operating hours of the equipment (6500h), and C is... P The current electricity price is set at 0.45 yuan / kWh. A f C These represent the probabilities of repair A and repair C for that year, respectively. A C C The repair budgets for A and C repairs are 60,000,000 yuan and 12,890,000 yuan respectively. malfunction Let be the average annual power outage time, r be the inflation rate (taken as 0.03), and R be the social discount rate (taken as 0.06).

[0044] Cost aggregation model: LCC = CI + CO + CM + CF + CD;

[0045] Where CI stands for investment cost, CO for operating cost, CM for maintenance cost, CF for failure cost, and CD for disposal cost.

[0046] An improved ADC algorithm model is used to perform real-time performance calculation and analysis on the electric generator. The performance model E(t) at time t is as follows:

[0047] E(t) = A * D(t) * C(t)

[0048] This includes: an availability matrix A calculated from parameters such as subsystem reliability, availability time, and failure rate; defining the availability states of the generator and its auxiliary equipment and the probability of state transitions between states; using the Markov state transition method to predict and calculate the state transition matrix D(t) for emergency, major, and general failures of the generator and its auxiliary equipment at the current assessment time t; and calculating the comprehensive assessment inherent capability matrix C(t) for the generator's grid-connected operation using subjective and objective evaluation methods. The cost and efficiency modules are coupled through real-time operation, and the cost-efficiency relationship is expressed as a cost-effectiveness ratio. An optimal maintenance plan is formulated based on the comprehensive maintenance cost input and efficiency changes.

[0049] Maintenance Decision Evaluation Module: This module uses the cost-effectiveness calculation module to determine the maintenance cost, efficiency improvement, and optimal cost-effectiveness scheme for the electric generator during maintenance. It also comprehensively considers the operational risks of the generator equipment to optimize maintenance decisions. Specifically, the cost-effectiveness calculation module selects appropriate time points and determines reasonable maintenance plans based on the evaluation results of the electric generator's operational performance indicators. It employs a condition-based maintenance approach that optimizes overall cost-effectiveness, and, due to operational risk assessments, may require increased cost investment to improve efficiency when necessary.

[0050] The display and interaction module includes the decision-making content derived from the maintenance decision evaluation module through calculations, including the maintenance level and optimal maintenance time. The maintenance level is divided into minor repair, major repair, and equipment replacement, which are presented on the display interface. The module also visually presents the cost and efficiency changes of the electric generator throughout its entire life cycle through cost curves, efficiency curves, and cost-effectiveness analysis curves, as well as the corrective effects of different past maintenance decisions. Furthermore, it displays the predictive evaluation of the effectiveness of preventative maintenance in the current state.

[0051] A cost-efficiency assessment system for the entire life cycle of an electric generator, designed for maintenance, consists of an equipment data acquisition module that collects and extracts the underlying data of the electric generator, a cost-efficiency calculation module that performs specific calculations on maintenance costs and changes in operational efficiency, a maintenance decision assessment module that analyzes the benefits of a single maintenance operation and the optimization effect of the equipment's entire life cycle management based on the calculation results, and finally, an information-based and visual display of the assessment decision results, the evaluation of preventive maintenance effects, and the cost-efficiency change curves in the display and interaction module.

[0052] A maintenance-oriented life-cycle cost-efficiency assessment system for electric generators optimizes equipment operation and maintenance by calculating generator costs, efficiency, cost-efficiency relationships, and analyzing and assessing operational risks. This reduces the cost-efficiency ratio and operational risks throughout the equipment's lifecycle. It achieves comprehensive optimization of the economy and reliability of equipment management, significantly improving the life-cycle management model for power equipment.

[0053] In this embodiment, taking an electric generator in a power plant as an example, monitoring data and offline data of various components of a normally operating unit are used as data inputs. The cost-effectiveness curve of the unit for the entire year of 2016 is analyzed to provide a basis for design prediction decisions.

[0054] The efficiency calculation module divides the electric generator into four subsystems: mechanical system, main system, protection and control system, and speed regulation system, and treats them as a series system in terms of availability. All subsystems operating normally are defined as the normal available state. The unit is defined as the non-stop fault state when only the mechanical system fails, and all other states are defined as fault states.

[0055] Availability Matrix A: Based on historical data, the generator set's average time between failures (MTBF) and average repair time (MTBT) are obtained. The failure rates for each system are: Mechanical System P1 = 0.00142, Generator Main System P2 = 0.00145, Protection and Control System P3 = 0.00159, Speed ​​Regulation System P4 = 0.00163. Based on the above generator set status relationships, the availability matrix is ​​obtained.

[0056] A=[0.8983, 0.0894, 0.0123] (1)

[0057] Credibility matrix D: Based on the historical failure rate data of this generator set using the Weibull distribution. Failure rate models are established for each subsystem, and the model coefficients are as follows:

[0058]

[0059] Considering the impact of maintenance on electric generators and their auxiliary equipment, the retirement age method is used to quantify it as an efficiency improvement. Based on the changes in equipment failure rate after historical maintenance, the equivalent service life t is calculated by back-calculating using the Weibull distribution model. Based on empirical data, the retirement amount is averaged, with a retirement factor of g1 = 0.40 for major maintenance types and g2 = 0.15 for minor maintenance types.

[0060] The formula for calculating the service life reduction caused by major maintenance is as follows:

[0061]

[0062] In the formula: Δt1i T is the service life rollback time resulting from the i-th major overhaul; 1i The actual equipment operating time during the overhaul is represented by x, which represents the number of major type maintenance operations.

[0063] Similarly, the calculation method for service life reduction caused by minor maintenance is as follows:

[0064]

[0065] The formula for calculating equivalent service years is:

[0066]

[0067] Where T is the actual service years and t is the equivalent service years.

[0068] Then, through the reliability model... Obtain the reliability R i (t)(i=1,2,3,4). This yields the reliability matrix D(t) considering maintenance changes.

[0069]

[0070] The inherent capability matrix C is constructed using the operational data from the equipment data acquisition module, which outputs seven performance indicators including primary frequency regulation (FM) commissioning rate, response target deviation, FM dead zone, FM response rise time, FM response lag time, FM settling time, and permanent slip rate. The inherent capability matrix C = [C1, C2, C3, C4, C5, C6, C7] is then constructed using the fuzzy comprehensive evaluation method. The specific steps are as follows: An evaluation level set V = [1, 0.8, 0.6] is established based on the evaluation factors. The subjective weights of each indicator are determined using the subjective analytic hierarchy process (AHP). Then, the objective weights are determined using the objective entropy weight method. Finally, the subjective and objective weights are multiplied together to determine the indicator weight W. The weight table is as follows:

[0071]

[0072]

[0073] The fuzzy normal distribution membership function is used to establish the membership matrix R of each element in the rating set corresponding to each indicator:

[0074]

[0075]

[0076]

[0077] In the formula, μ min μ o μ maxThe three elements of the corresponding evaluation set V are u, where u is the indicator detection value.

[0078] The evaluation values ​​of each indicator are obtained by using C = W * R * V, thus constructing a real-time intrinsic capability matrix. C(t) is generated based on the data collected and monitored in each cycle of 2016.

[0079] From the efficiency model: E=A*D(t)*C(t) (8)

[0080] The performance evaluation results are shown in the table below:

[0081] Time t / (month) 1 2 3 4 Efficiency E(t) 0.79633 0.78891 0.68258 0.77911 Time t / (month) 5 6 7 8 Efficiency E(t) 0.76662 0.75596 0.74491 0.73356 Time t / (month) 9 10 11 12 Efficiency E(t) 0.75251 0.73676 0.60691 0.76558

[0082] In March, a minor maintenance was conducted because the efficiency value dropped below the warning threshold (0.68258 < 0.7). Based on the service life regression theory, the revised reliability matrix and inherent capability parameters were improved, resulting in enhanced equipment efficiency. In August, a preventative minor maintenance was conducted due to the changing trend of the efficiency value, and the effectiveness of this maintenance will be evaluated below. In November, due to a significant decrease in the unit's efficiency value, which approached the warning threshold (0.6), a major maintenance was conducted to ensure the safe and stable operation of the unit.

[0083] Cost calculation module: The loss function and boundary conditions of parameters intervened through experience are used as inputs to the Grey Wolf algorithm and initialized. Iterative optimization is performed until convergence.

[0084]

[0085] The detailed parameters are shown in the table below:

[0086] Unsolved parameters k <![CDATA[η energy ]]> <![CDATA[f A ]]> <![CDATA[f C ]]> <![CDATA[T malfunction ]]> optimal value 0.887 0.002 0.15 0.16 6.02

[0087] LCC calculation model:

[0088] LCC = CI + CO + CM + CF + CD (10)

[0089] To conduct a detailed analysis of maintenance decisions, when calculating the cost change LCC(t) during this period, only the cost change in 2016 was analyzed, and only the variable costs SC, including maintenance costs, failure costs, and operating costs, were statistically analyzed. The cost changes are shown in the table below:

[0090] Time t / (month) 1 2 3 4 SC(t) / (yuan) 8303 16606 160751 169054 Time t / (month) 5 6 7 8 SC(t) / (yuan) 177357 185660 193963 209284 Time t / (month) 9 10 11 12 SC(t) / (yuan) 217587 225890 890662 898965

[0091] The cost-effectiveness ratio fitting method can be used to obtain the annual cost-effectiveness changes of the unit. The smaller the cost-effectiveness ratio, the lower the unit's operation and maintenance costs and the better its efficiency. Furthermore, the cost-effectiveness ratio will inevitably show an increasing trend over time. The cost-effectiveness results obtained from the above data are shown in the table below:

[0092] Time t / (month) 1 2 3 4 LCC(t) / E(t) 1.042 2.100 23.54 21.898 Time t / (month) 5 6 7 8 LCC(t) / E(t) 23.153 24.551 26.030 28.530 Time t / (month) 9 10 11 12 LCC(t) / E(t) 28.915 30.600 146.739 117.423

[0093] To quantify the maintenance effect and comprehensively consider both economic efficiency and operational superiority, the cost change of the unit equipment can be compared with the efficiency improvement; the smaller the value, the better the overall effect. The results are as follows:

[0094]

[0095] Maintenance Decision Analysis: Based on the above cost input, efficiency changes, and cost-effectiveness ratio, the following analysis can be made: The maintenance cost-effectiveness ratio in August was the best, achieving a good efficiency improvement with the lowest cost input, indicating that timely preventive maintenance can improve equipment availability and has good economic benefits; the maintenance in March achieved better efficiency improvement with relatively low cost input; the maintenance in November was a decision made based on risk assessment due to low efficiency. According to the cost-effectiveness ratio, it had lower economic benefits, but it was necessary for the safe and reliable operation of the generator.

[0096] Example 2:

[0097] In this embodiment, the 2018 data of the same generator set equipment in Embodiment 1 is used as an example.

[0098] Since the equipment availability data, Weibull data, and cost aggregation model coefficients are derived from full lifecycle data processing, they have broad applicability. Therefore, the expressions for the availability matrix A, the reliability matrix D, and the variable cost calculation module SC in this embodiment are the same as in Embodiment 1.

[0099] The following table summarizes the variable cost data for 2018:

[0100] Time t / (month) 1 2 3 4 SC(t) / (yuan) 8303 16606 24909 33212 Time t / (month) 5 6 7 8 SC(t) / (yuan) 41515 606285 614588 622891 Time t / (month) 9 10 11 12 SC(t) / (yuan) 631194 639574 739582 747885

[0101] Using maintenance information and actual service life t as inputs, the equivalent service life is obtained by substituting them into the service life regression model. The performance evaluation results for each evaluation period are obtained from the indicator data of the evaluation period using the performance evaluation model (8), as shown in the table below:

[0102] Time t / (month) 1 2 3 4 Efficiency E(t) 0.8022 0.7496 0.7006 0.6753 Time t / (month) 5 6 7 8 Efficiency E(t) 0.6548 0.4654 0.8181 0.7797 Time t / (month) 9 10 11 12 Efficiency E(t) 0.7432 0.7084 0.6309 0.7649

[0103] The cost-effectiveness data obtained from this are shown in the table below:

[0104] Time t / (month) 1 2 3 4 LCC(t) / E(t) 1.035 2.215 3.555 4.918 Time t / (month) 5 6 7 8 LCC(t) / E(t) 6.340 130.272 75.124 79.888 Time t / (month) 9 10 11 12 LCC(t) / E(t) 84.929 90.284 117.226 97.775

[0105] The table shows a sharp increase in the cost-effectiveness ratio in June and November. This is due to the cost of repairs incurred after equipment malfunctions. To quantify the effectiveness of repairs, and considering both economic efficiency and operational performance, the cost change of the unit equipment can be compared with the efficiency improvement. The smaller the value, the better the overall effect. The results are as follows:

[0106]

[0107] Maintenance decision analysis: Based on the cost-effectiveness ratio, it is clear that the maintenance that occurred in November achieved better maintenance results with less cost, and the input-output ratio was better. However, the maintenance that occurred in June was a necessary maintenance measure due to the sharp decline in equipment efficiency, which may cause operational safety problems.

[0108] Example 3:

[0109] In this embodiment, the 2019 data of the same generator set equipment in Embodiment 1 is used as an example.

[0110] The following table summarizes the variable cost data for 2019:

[0111] Time t / (month) 1 2 3 4 SC(t) / (yuan) 8303 16606 24909 33212 Time t / (month) 5 6 7 8 SC(t) / (yuan) 41515 49823 637346 645649 Time t / (month) 9 10 11 12 SC(t) / (yuan) 653952 765952 774255 782558

[0112] The performance evaluation results for each evaluation period are shown in the table below:

[0113] Time t / (month) 1 2 3 4 Efficiency E(t) 0.8195 0.7807 0.7438 0.7371 Time t / (month) 5 6 7 8 Efficiency E(t) 0.7086 0.6886 0.5516 0.7963 Time t / (month) 9 10 11 12 Efficiency E(t) 0.6824 0.6319 0.7449 0.7124

[0114] The cost-effectiveness data obtained from this are shown in the table below:

[0115] Time t / (month) 1 2 3 4 LCC(t) / E(t) 1.013 2.127 3.349 4.687 Time t / (month) 5 6 7 8 LCC(t) / E(t) 6.029 9.034 80.038 87.593 Time t / (month) 9 10 11 12 LCC(t) / E(t) 95.831 121.213 103.941 109.848

[0116] The cost input and efficiency improvement results of the two maintenance operations are compared as follows:

[0117]

[0118] Results Analysis: Due to equipment aging, the efficiency value was lowered to 0.65. Because the unit's efficiency value was low in July, maintenance was performed. However, the cost-effectiveness ratio shows that the cost-benefit ratio was lower than that of the maintenance in October, although it was a necessary maintenance measure taken due to potential safety issues during operation.

[0119] The present invention has been described in detail above through embodiments. These embodiments do not constitute a limitation on the scope of the invention. All technical solutions or method improvements made by those skilled in the art based on the technical solutions described in this invention, and within the scope and protection of this invention, are protected by this patent.

Claims

1. A life-cycle cost-effectiveness assessment system for electric generators oriented towards maintenance, characterized in that, It includes a sequentially connected equipment data acquisition module, cost-efficiency calculation module, maintenance decision evaluation module, and display and interaction module; The equipment data acquisition module is equipped with a data acquisition interface to collect and extract data records from the financial system, operation ticket system, and SCADA system throughout the equipment's entire lifecycle. This enables real-time monitoring of equipment operation and cost data, which is then transmitted to the cost-efficiency calculation module. The maintenance decision evaluation module is used to calculate the resource allocation of the maintenance plan based on the equipment cost-efficiency calculation results, conduct an economic evaluation of the equipment's entire life cycle, optimize the management plan based on the full life cycle records, and transmit the optimized plan and evaluation results to the display and interaction module. The display and interaction module is used to obtain the full life cycle cost and performance evaluation scheme of electric generators, to visualize the operation and maintenance logs, condition-based maintenance predictions and preventive maintenance scheme evaluations, and to set up interfaces to provide human-computer interaction functions, providing information-based guidance for real-time operation and maintenance management. In the cost-effectiveness calculation module, based on equipment operation and cost data, the module calculates the maintenance cost input and operational efficiency changes involving different types of maintenance content, and comprehensively represents the cost and efficiency situation through cost-effectiveness ratio. The maintenance cost input, efficiency changes and cost-effectiveness ratio calculation results are then transmitted to the maintenance decision evaluation module. The calculation of operational efficiency changes is based on the ADC model method. The cost-effectiveness ratio is the ratio of maintenance costs to operational efficiency of an electric generator at the same point in time. In the cost-efficiency calculation module, a life-cycle cost estimation model for electric generators is established; Based on the availability, reliability, and inherent capability module results obtained from the ADC model, a comprehensive evaluation result of equipment performance is defined. , by formula Solving for the efficiency value of the electric generator system ; Considering the impact of maintenance on electric generators, the equivalent service life is calculated using the service life rollback method. t Then utilize equivalent years of service t The credibility D matrix is ​​corrected to obtain the post-maintenance performance value, thus achieving a closed-loop interaction between maintenance decisions and the evaluation of those decisions. The correction of the credibility D matrix includes: Through reliability model Obtain reliability , =1,2,3,4, thus obtaining the reliability matrix considering maintenance changes. , ； The cost-effectiveness calculation module outputs results to the maintenance decision module for formulating various maintenance decisions. Based on these decisions, the module uses a service life rollback method to predict the failure rate of the generator after maintenance. The prediction results are then output to the cost-effectiveness calculation module, which calculates the cost-effectiveness ratio of the various maintenance decisions. The calculation results are also output to the maintenance decision module. Finally, the module selects the optimal maintenance decision from among the various options, choosing the maintenance decision with the lowest cost-effectiveness ratio while ensuring the safe and reliable operation of the equipment. The optimal maintenance decision is then output to the display and interaction module.

2. The life-cycle cost-effectiveness assessment system for electric generators oriented towards maintenance, as described in claim 1, is characterized in that: In the cost-efficiency calculation module, based on industry implementation cases of full life cycle cost estimation for electric generators and cost parameter characteristics, targeted modeling is performed for various costs, including investment cost CI, operating cost CO, maintenance cost CM, failure cost CF, and scrap cost CD. Lifecycle cost (LCC) includes: ； The calculation of maintenance costs is based on historical data statistics and uses a heuristic prediction algorithm to predict trends. As the historical cost records increase, the correction parameters of the heuristic prediction algorithm are iteratively updated, and the maintenance cost prediction results are updated in each iteration.

3. The life-cycle cost-effectiveness assessment system for electric generators oriented towards maintenance, as described in claim 1, is characterized in that: The equipment data acquisition module is equipped with hardware sensors and data interfaces that are associated with financial statistics, two-ticket records, and SCADA equipment operation data. It uses intelligent text recognition and collects and aggregates data based on a unified communication networking protocol. The data is classified and normalized through a data platform and mapped to the device tree. The data is then exported as a unified CSV file for easy access and analysis by the calculation module.

4. The life-cycle cost-efficiency assessment system for electric generators oriented towards maintenance as described in claim 1, characterized in that: In the cost-efficiency calculation module, an electric generator system equipment architecture model is established; the electric generator unit system is defined to include: the electric generator body, air cooling system, upper and lower guides and thrust bearings, braking and stopping system, as well as electric heaters, fire extinguishing system and auxiliary systems; the electric generator body includes the stator, rotor and its electrical components; The calculation of operational efficiency changes is based on the ADC model method. An inherent capability index system for electric generators is established, and the weights of each index in the inherent capability system are determined by the analytic hierarchy process and the entropy weight method. The inherent capability matrix C is then calculated. This paper defines the availability states of electric generators and their auxiliary equipment, as well as the probability of state transitions between these states. Using the Markov state transition method, it predicts and calculates emergency, major, and general faults of the electric generators and their auxiliary equipment, applying the results to determine preventative maintenance. Based on the performance indicators for grid-connected operation of the electric generators, it employs a subjective and objective evaluation method to comprehensively assess the prediction results. Maintenance decisions are then made based on the assessment results. Finally, using the ADC model, it obtains the real-time performance changes of the electric generators and their auxiliary equipment before and after implementing the formulated maintenance decisions throughout their entire lifecycle.

5. The maintenance-oriented full life-cycle cost-effectiveness assessment system for electric generators as described in claim 3, characterized in that: The availability A matrix is ​​calculated as follows: Regarding the operating states of an electric generator system, it is divided into three states: normal operation, non-stop fault, and fault state, which are represented by numbers. , , The availability vector of the electric generator system is then represented as: Availability status The calculations were performed based on the functional architecture coordination relationship and reliability theory of electric generators. i= 1, 2, 3; The credibility matrix D is as follows: Credibility is defined as... This indicates that the unit system is in a state of... Change to status The probability; based on the constraints calculated by the credibility calculation, the state transition matrix of the electric generator satisfies , That is, the state transition during the execution of the switching task is a unidirectional, one-time change, and conforms to the randomness of Markov state transition; the credibility matrix D is obtained by using the Laplace transform method. The inherent performance indicators of electric generators include the assessment indicators of each functional component of the electric generator and its auxiliary equipment. Based on the power system equipment performance evaluation standards, the performance characteristics of electric generators are evaluated, including output ramp-up speed and transient steady-state characteristics. The fuzzy comprehensive evaluation method is used to calculate the inherent capability C matrix. The specific process is as follows: Establish an evaluation level set V based on the set of evaluation factors; calculate subjective weights using the AHP method and objective weights using the entropy weight method, combine the weights, and establish a weight set W; generate a membership degree matrix R for each element in the corresponding evaluation level set V according to the membership degree of the evaluation indicators; and then, using the formula... The inherent capability matrix C is obtained by solving the problem.

6. The maintenance-oriented full life-cycle cost-effectiveness assessment system for electric generators as described in claim 5, characterized in that: In the maintenance decision-making module, based on the maintenance cost input and efficiency value change results obtained from the cost-efficiency calculation module, a cost-efficiency coupled analysis is performed on the electric generator equipment system. The benefits of a single maintenance decision scheme are evaluated according to the optimal cost-efficiency ratio method. At the whole life cycle scale, maintenance cost input, efficiency improvement, cost-efficiency ratio and operational risks are considered simultaneously. Maintenance decisions are made by balancing economy and reliability. Based on historical data and service life rollback methods, the cost-efficiency ratio benefits of maintenance decisions are predicted to guide the optimization of maintenance decisions.

7. A maintenance-oriented life-cycle cost-effectiveness assessment system for electric generators as described in any one of claims 1 to 6, characterized in that: In the display and interaction module, a human-computer interaction module is set up based on the electric generator operation log, the maintenance prediction output by the maintenance decision evaluation module, and the maintenance evaluation results. By displaying cost curves, efficiency curves, and cost-effectiveness curves, the module intuitively presents the operation and maintenance benefits of the electric generator throughout its entire life cycle, providing digital and visual interactive functions for operation and maintenance management personnel to carry out maintenance business.

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