A risk assessment method and system for a thermal power plant with a redundant system

Abstract

The application provides a risk assessment method and system for a thermal power equipment with a redundant system, relates to the field of thermal power generation technology, and comprises the following steps: obtaining characteristic parameters of each equipment in a target system; determining an emergency coefficient of each equipment according to an equipment importance parameter, an equipment health state parameter and an equipment real-time reliability parameter of each equipment; determining a comprehensive emergency coefficient of a voting system based on a voting number and a total unit number of the voting system and the emergency coefficient of each equipment; and determining a comprehensive risk index based on the comprehensive emergency coefficient of the voting system, so as to realize scientific quantification of the overall risk of a complex system with a backup or redundant configuration.

Description

Technical Field

[0001] This invention relates to the field of thermal power generation technology, and more specifically, to a risk assessment method and system for thermal power equipment with redundant systems. Background Technology

[0002] In the graded management system for thermal power equipment, some systems (such as pulverizing systems and steam systems) have multiple pieces of equipment operating in parallel and a certain number of spares.

[0003] Existing methods for hierarchical management of thermal power equipment mostly rely on series or simple parallel models for system risk assessment. For example, series models assume all equipment must be functioning normally for the system to operate normally, leading to an overestimation of the risk of redundant systems. Conversely, simple parallel models assume the system only fails when all equipment fails, resulting in an underestimation of the actual system risk. Therefore, simplification based on series or parallel models leads to inaccurate calculations of the overall system risk index, consequently affecting the scientific judgment of equipment maintenance priorities and the rational allocation of maintenance resources. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a risk assessment method and system for thermal power equipment with redundant systems, so as to improve the accuracy of system risk assessment and rationally allocate maintenance resources.

[0005] Firstly, this application provides a risk assessment method for thermal power equipment with redundant systems, including: Obtain the characteristic parameters of each device in the target system; the characteristic parameters include system connectivity, device importance parameters, device health status parameters, and device real-time reliability parameters; the system connectivity includes the voting system; The urgency coefficient of each device is determined based on its importance parameters, health status parameters, and real-time reliability parameters. The overall emergency coefficient of the voting system is determined based on the number of votes and the total number of units in the voting system, as well as the emergency coefficient of each device. The comprehensive risk index is determined based on the comprehensive urgency coefficient of the voting system.

[0006] Optionally, the overall urgency coefficient of the voting system is determined, including: Based on the urgency coefficient of each device in the voting system, determine the non-urgency coefficient of each device. The non-urgent coefficient of the voting system is determined based on the number of votes, the total number of units, and the non-urgent coefficient of each device. The overall urgency coefficient of the voting system is determined based on the non-urgency coefficient of the voting system.

[0007] Optionally, the non-urgent factor of the voting system is determined, including: When the non-urgent coefficients of all devices in the voting system are the same, the non-urgent coefficients of the voting system are determined based on the combination coefficients corresponding to each value within the range of the number of votes to the total number of units and the non-urgent coefficients of each device. When the non-urgent coefficients of each device in the voting system are different, the non-urgent coefficients of the voting system are determined based on the different combinations of normal working states of each device and their corresponding non-urgent coefficients.

[0008] Optionally, the risk assessment method for thermal power equipment with redundant systems provided in this application further includes: When the number of votes equals the total number of units, the voting system is treated as a series system, and the overall urgency coefficient of the voting system is determined by the series system. When the number of votes is 1, the voting system is treated as a parallel system, and the overall urgency coefficient of the voting system is determined by the parallel system.

[0009] Optionally, the risk assessment method for thermal power equipment with redundant systems provided in this application further includes: When the voting system is used as a series system, the overall emergency coefficient of the series system is determined based on the emergency coefficient of each device, and the overall risk index is determined based on the overall emergency coefficient of the series system. When the voting system is used as a parallel system, the overall emergency coefficient of the parallel system is determined based on the emergency coefficient of each device, and the overall risk index is determined based on the overall emergency coefficient of the parallel system.

[0010] Optionally, determine the equipment importance parameters for each device, including: Determine multiple assessment metrics for each device to evaluate its importance; The weighting coefficient for each evaluation indicator is determined based on multiple evaluation indicators. The importance parameters for each device are determined based on multiple evaluation indicators and multiple weighting coefficients.

[0011] Optionally, determine the equipment health status parameters for each device, including: Determine the equipment type for each piece of equipment, which includes rotating machinery and non-rotating machinery. For rotating machinery, the equipment status is determined based on the vibration parameters of the rotating machinery, and the equipment health status parameters are determined based on the equipment status. For non-rotating mechanical equipment, control limits are determined based on the moving average value and range of the process parameter measurement points of the non-rotating mechanical equipment. The equipment status of the non-rotating mechanical equipment is determined based on the control limits, and the equipment health status parameters are determined based on the equipment status.

[0012] Secondly, this application provides a risk assessment system for thermal power equipment with redundant systems, including: The data acquisition module is used to acquire the characteristic parameters of each device in the target system. The characteristic parameters include system connectivity, device importance parameters, device health status parameters, and device real-time reliability parameters. The system connectivity includes the voting system. The risk assessment module is used to determine the urgency coefficient of each device based on its importance parameters, health status parameters, and real-time reliability parameters; to determine the comprehensive urgency coefficient of the voting system based on the number of votes and the total number of units, as well as the urgency coefficient of each device; and to determine the comprehensive risk index based on the comprehensive urgency coefficient of the voting system.

[0013] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the aforementioned risk assessment method for thermal power equipment with redundant systems.

[0014] Fourthly, this application provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the aforementioned risk assessment method for thermal power equipment with redundant systems.

[0015] This invention provides a risk assessment method and system for thermal power equipment with redundant systems. The method involves acquiring characteristic parameters of each device in the target system; determining the urgency coefficient of each device based on its importance parameters, health status parameters, and real-time reliability parameters; determining the comprehensive urgency coefficient of the voting system based on the number of votes and the total number of units, as well as the urgency coefficient of each device; and determining a comprehensive risk index based on the comprehensive urgency coefficient of the voting system. This enables the scientific quantification of the overall risk of complex systems with backup or redundancy configurations.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1A flowchart of a risk assessment method for thermal power equipment with redundant systems provided by an embodiment of the present invention is shown; Figure 2 A schematic diagram of the powder-making system provided in an embodiment of the present invention is shown; Figure 3 This diagram illustrates the structure of a risk assessment system for thermal power equipment with a redundant system, provided by an embodiment of the present invention. Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present invention is shown. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0020] To facilitate a better understanding of this application by those skilled in the art, the technical terms used in this application will be briefly introduced below.

[0021] The target system is a whole composed of multiple devices combined according to specific functional logic that requires risk assessment, such as the flue gas system, pulverizing system or steam valve system in a thermal power plant. In this application, risk assessment of the target system is conducted to guide maintenance decisions for its internal equipment.

[0022] Equipment importance parameter is an indicator used to quantify the critical position of equipment in a target system, reflecting the comprehensive impact that equipment failure may have on personnel safety, system function, power generation and maintenance economy. In this application, the equipment importance parameter is usually a relatively fixed value determined by the inherent attributes of the equipment.

[0023] Equipment health status parameters are indicators used to quantitatively characterize the current actual operating status of equipment, reflecting the degree of deterioration, defects, or abnormal states that have already existed in the equipment during operation. In this application, the equipment health status parameter is a dynamically changing value, and the magnitude of the value depends on the results of online condition monitoring (such as vibration and temperature) or offline condition monitoring (such as periodic tests and inspections).

[0024] The real-time reliability parameter of an equipment is an indicator used to quantitatively characterize the ability of an equipment to perform its predetermined functions without failure over a period of time in the future, so as to reflect the uncertainty or risk trend of equipment failure. In this application, the real-time reliability parameter of the equipment is different from the health status parameter that characterizes the current state. The real-time reliability parameter focuses on the prediction of the future operational reliability of the equipment and is a dynamic prediction value.

[0025] The urgency factor is used to comprehensively measure the urgency of a single device at the current moment. In this application, the urgency factor is a dimensionless parameter calculated based on the device's importance parameter, device health status parameter, and device real-time reliability parameter. The magnitude of the urgency factor is positively correlated with the importance of the device, the degree of degradation of the current state, and the probability of future failure. That is, the more important the device, the worse its state, and the more likely it is to fail in the future, the higher the urgency factor.

[0026] The comprehensive risk index is used to comprehensively measure the overall risk level of the entire target system at the current moment. In this application, the comprehensive risk index is a quantitative indicator obtained by comprehensively calculating based on the urgency coefficient of each device in the target system and combined with the system connection relationship between each device in the target system (such as series, parallel, mixed or voting). The magnitude of the comprehensive risk index reflects the possibility of overall system failure and the severity of the consequences.

[0027] A parallel system refers to a system in which devices are functionally redundant with each other. In this application, in a parallel system, as long as at least one device is working properly, the system as a whole can maintain normal operation; the system will only fail when all the parallel devices fail. For example, two wind turbines that are redundant with each other constitute a parallel system.

[0028] A series system refers to a system in which all devices are functionally connected end-to-end and indispensable. In this application, in a series system, all devices must be working properly for the entire system to function normally; if any one device fails, the entire system will fail. For example, a coal pulverizing subsystem consisting of a raw coal bunker, a coal feeder, and a coal mill connected in sequence constitutes a series system.

[0029] A voting system is a special type of redundant system connection, denoted as an "m / n(G) system," where n represents the total number of units constituting the subsystem, and m represents the minimum number of units required for the system to function normally, i.e., the number of votes. In a voting system, the system can maintain normal operation as long as at least m units are functioning normally; the system only fails when the number of normally functioning units is less than m. In this application, in a five-operation-one-standby pulverizing system, the boiler can operate normally as long as at least five of the six pulverizing subsystems are functioning normally; this system constitutes a voting system.

[0030] After introducing the technical terms used in this application, the technical solution provided in this application will be described in detail below.

[0031] This application provides a risk assessment method for thermal power equipment with redundant systems. (See attached document.) Figure 1 As shown, the risk assessment method for thermal power equipment with redundant systems provided in this application includes at least the following steps: Step 110: Obtain the characteristic parameters of each device in the target system; the characteristic parameters include system connection relationship, device importance parameters, device health status parameters, and device real-time reliability parameters; the system connection relationship includes the voting system.

[0032] In this embodiment, multiple evaluation indicators are determined for each device to assess its importance; a weighting coefficient is determined for each evaluation indicator; an importance parameter for each device is determined based on the multiple evaluation indicators and the multiple weighting coefficients; and the device type of each device is determined, including rotating machinery and non-rotating machinery. For rotating machinery, the device state is determined based on the vibration parameters of the rotating machinery, and a health status parameter is determined based on the device state. For non-rotating machinery, control limits are determined based on the moving average value and moving range of the process parameter measurement points of the non-rotating machinery, the device state is determined based on the control limits, and a health status parameter is determined based on the device state.

[0033] Specifically, the importance parameters of the equipment are determined by comprehensively and quantitatively scoring the multidimensional impacts that may be caused by equipment failure; the health status parameters of the equipment are determined by comprehensively analyzing the multi-source status monitoring data of the equipment; and the real-time reliability parameters of the equipment are determined by constructing an equipment reliability prediction model and calculating it based on real-time operating data. Furthermore, the specific process for determining equipment importance parameters through comprehensive quantitative scoring of the multidimensional impacts that equipment failure may cause is as follows: First, an evaluation index system is established based on eight dimensions, including the degree of impact of failure on personnel safety, the degree of impact of failure on system function, the frequency of equipment failure, the cost of equipment repair, the production loss caused by equipment downtime, the duration of equipment downtime, the ease of repair implementation, and the monitorability of equipment status. Second, specific scoring standards are set for each dimension. For example, for the impact of failure on personnel safety, different scores can be assigned based on factors such as whether it may lead to personal injury or death and the scale of such injury or death; for the impact of failure on system function, scores can be assigned based on factors such as whether equipment failure leads to system downtime and the scope of downtime. Then, the scores of each dimension are weighted and summed or comprehensively calculated to obtain the overall importance score of the equipment. Finally, the overall importance score is normalized or mapped to a specific numerical range to obtain the final equipment importance parameter. The higher the equipment importance parameter value, the higher the criticality of the equipment in the system, and the more severe the impact on power plant safety and production once a failure occurs. The specific process of determining equipment health status parameters through comprehensive analysis of multi-source condition monitoring data is as follows: For rotating equipment, an optimized vibration threshold standard is used for evaluation based on vibration monitoring data. This involves collecting vibration signals during equipment operation and extracting time-domain characteristic parameters such as vibration displacement, vibration velocity, and vibration acceleration from the vibration signals. Simultaneously, frequency-domain characteristic parameters are obtained through spectral analysis of the vibration signals, including the amplitude and phase information of each characteristic frequency. The time-domain characteristic parameters are compared with preset time-domain alarm thresholds and danger thresholds, and the amplitude of key frequencies in the frequency-domain characteristic parameters is compared with preset spectral alarm thresholds. Based on the degree of exceedance of the time-domain characteristic parameters and the severity of the fault characteristics reflected by the frequency-domain characteristic parameters, the health status level of the equipment is comprehensively determined. The health status level is divided into four levels: normal, alert, abnormal, and critical. Each level corresponds to a different health status score, thus obtaining the equipment health status parameters. For non-rotating equipment, real-time process parameter data is obtained from the plant-level monitoring information system. The process parameter data includes temperature, pressure, flow, and liquid level parameters. For each process parameter, the absolute deviation between its real-time measured value and the center value of the normal operating range, as well as the variance of the rate of change of the parameter within a preset time window, are calculated to obtain the deviation degree index and the trend stability index, respectively. The deviation degree index is compared with a preset deviation threshold, and the trend stability index is compared with a preset fluctuation threshold. Based on the comparison results, the health status level of the equipment is determined. The health status level is divided into four levels: normal, alert, abnormal, and critical. The normal state indicates that all parameters are within the normal range and the fluctuation is stable. The alert state indicates that a single parameter has a slight deviation or the fluctuation is increased. The abnormal state indicates that a single parameter is seriously out of control or multiple parameters have slight deviations at the same time. The critical state indicates that multiple parameters are seriously out of control or critical parameters have dangerous deviations. For devices lacking online monitoring data, supplement the missing data by combining offline status monitoring results or regular inspection records; if valid data still cannot be obtained, assign the device a default value according to the normal state. Map the determined health status level to specific health status parameter values: normal state is mapped to the first value, note state is mapped to the second value, abnormal state is mapped to the third value, and severe state is mapped to the fourth value. The first value is less than the second value, the second value is less than the third value, and the third value is less than the fourth value. The higher the health status parameter value, the worse the current health status of the device, and the greater the possibility of malfunction or performance degradation. The specific process of constructing a reliability prediction model for equipment and calculating and determining the real-time reliability parameters of the equipment based on real-time operating data is as follows: First, acquire historical operating data, historical fault data, maintenance record data, and reliability statistics of similar equipment to establish a basic reliability database for the equipment. Historical operating data includes the equipment's commissioning date, cumulative operating hours, number of start-ups and shutdowns, and load rate change records. Historical fault data includes fault occurrence time, fault mode, fault location, and fault repair time. Maintenance record data includes maintenance date, maintenance type, list of replaced parts, and post-maintenance trial operation data. Second, select an appropriate reliability prediction model based on the equipment type and data conditions. For equipment with clear lifespan distribution characteristics and complete fault data, a Weibull model can be used. The Weibull distribution model describes the bathtub curve characteristics of equipment failure rate over time. The Weibull distribution model contains two undetermined parameters: shape and scale. For equipment with obvious state transition characteristics and multi-state monitoring data, the Markov model can be used to describe the transition probability between normal, degraded, and fault states. The Markov model contains one undetermined parameter: the state transition probability matrix. Then, historical data is used to train and calibrate the model parameters. For the Weibull distribution model, the maximum likelihood estimation method or rank regression method is used to estimate the shape and scale parameters. For the Markov model, the frequency statistics method or maximum entropy method is used to estimate the state transition probability matrix. After the parameter estimation is completed, the goodness-of-fit test or the Akaike information criterion is used to validate the model. Only after the validation is passed can the model be put into use. During the real-time calculation phase, the current operating time and cumulative operating hours of the equipment are mapped to the time variables of the Weibull distribution model, or the current health status level of the equipment is mapped to the current state of the Markov model. The number of start-stop cycles is used as a correction factor for the equivalent operating time, and post-maintenance trial operation data is used as the basis for adjusting the initial reliability value. This data is input into the prediction model to calculate the probability that the equipment will complete its specified functions within a preset time interval in the future. This preset time interval is determined based on the equipment's maintenance cycle or risk assessment requirements, typically one month or one quarter. For equipment with an abnormal or severe health status, the reliability prediction value output by the model is adjusted downwards according to a preset health status correction coefficient, which is determined based on the correlation analysis results between the health status level and historical failure data. For equipment that has resumed operation after maintenance, the initial reliability value of the model is adjusted upwards or reset to the factory level based on the maintenance quality rating and the performance parameters met during the trial operation. The final real-time reliability parameter is expressed as a percentage; the higher the parameter value, the better the equipment's reliability level and the lower the probability of failure within the preset time interval in the future.

[0034] Step 120: Determine the urgency coefficient of each device based on its importance parameters, health status parameters, and real-time reliability parameters.

[0035] In this embodiment, determining the urgency coefficient of each device includes: determining the initial urgency of each device based on device importance parameters and device health status parameters; determining the real-time urgency of each device based on the initial urgency and device real-time reliability parameters; and determining the urgency coefficient of each device based on its real-time urgency. Further, the real-time urgency is obtained by multiplying the initial urgency by a preset coefficient; the preset coefficient is determined based on the device's real-time reliability parameters.

[0036] Specifically, the initial urgency of the equipment is obtained by multiplying the equipment importance parameter by the equipment health status parameter. The initial urgency can comprehensively reflect the inherent importance of the equipment and its current degree of degradation. The higher the initial urgency, the more important the equipment is and the worse its current condition is, requiring close attention. To mitigate the risk of future equipment failure, this application calculates the real-time urgency by multiplying an initial urgency level by a preset coefficient. This preset coefficient can be determined using the equipment's real-time reliability parameter. For example, the preset coefficient can be set as the reciprocal of the real-time reliability parameter or a value that corresponds to it. Therefore, when the equipment's real-time reliability parameter is high (i.e., high reliability), the preset coefficient is small, resulting in a low real-time urgency level, indicating that immediate intervention is not required. Conversely, when the equipment's real-time reliability parameter is low (i.e., high failure risk), the preset coefficient is large, resulting in a higher real-time urgency level, thus highlighting the urgency of equipment failure in the short term. After determining the real-time urgency of the equipment, further processing is performed to obtain the equipment's urgency coefficient. For example, the real-time urgency can be mapped to a standard numerical range, or a pre-defined transformation function (such as normalization or complementation) can be used to generate the urgency coefficient. This allows for a standardized and easily comparable numerical value to comprehensively measure the risk urgency of a single piece of equipment at the current moment. The higher the urgency coefficient, the higher the priority of intervention required due to the equipment's high importance, poor current condition, and high probability of future failure. Through this process, the static importance of the equipment, its current dynamic health status, and the prediction of future operational risks are organically combined to form a quantitative indicator that can sensitively reflect the dynamic changes in equipment risk. This provides accurate and reliable input for subsequent system-level risk assessment and maintenance decisions.

[0037] Furthermore, in this embodiment, the urgency factor of the device can be determined using the following expression:

[0038]

[0039] In the formula, The emergency factor of the equipment. To determine the real-time urgency of the equipment, For the equipment's health status parameters, For equipment importance parameters, These are the real-time reliability parameters of the equipment.

[0040] Step 130: Determine the overall emergency coefficient of the voting system based on the number of votes and the total number of units in the voting system, as well as the emergency coefficient of each device.

[0041] Step 140: Determine the comprehensive risk index based on the comprehensive urgency coefficient of the voting system.

[0042] In this embodiment, the non-urgent coefficient of each device in the voting system is determined based on its urgency coefficient; the non-urgent coefficient of the voting system is determined based on the number of votes, the total number of units, and the non-urgent coefficient of each device; and the comprehensive urgency coefficient of the voting system is determined based on its non-urgent coefficient. Specifically, when the non-urgent coefficients of each device in the voting system are the same, the non-urgent coefficient is determined based on the combination coefficients corresponding to each value within the range from the number of votes to the total number of units, and the non-urgent coefficient of each device. When the non-urgent coefficients of each device in the voting system are different, the non-urgent coefficient is determined based on different combinations of the normal operating states of each device and their corresponding non-urgent coefficients.

[0043] In the embodiments of this application, such as Figure 2 As shown, the pulverizing system includes multiple raw coal bunkers, coal feeders, and coal mills. The raw coal bunker A, coal feeder A, and coal mill A are connected in series. If any one of these three equipment malfunctions, the entire pulverizing system will stop operating. However, there are a total of six pulverizing systems: A, B, C, D, E, and F. Under normal circumstances, the output of five pulverizing systems is sufficient to meet the boiler's combustion needs, and one pulverizing system is in standby mode. When a system malfunctions and stops operating, the standby equipment can be immediately activated.

[0044] When the target system is a voting system, firstly, obtain the urgency coefficient of each device that constitutes the voting system; based on the urgency coefficient of each device, determine the non-urgency coefficient of each device. Secondly, the non-urgent coefficient of the voting system is determined based on the number of votes, the total number of units, and the non-urgent coefficient of each device. When the non-urgent coefficients of all devices constituting the voting system are the same, meaning all devices have the same failure risk, a combination probability-based method can be used to determine the non-urgent coefficient. Specifically, for each possible number of normally functioning units within the range from the number of votes to the total number of units, the probability that the system can function normally with that number of normally functioning units is calculated. The probability corresponding to a certain number of normally functioning units is equal to the number of combinations of selecting that number of units from the total number of units, multiplied by the product of the probability that those units function normally and the probability that the remaining units fail. Since the non-urgent coefficients of all devices are the same, the probability of a device functioning normally equals the urgent coefficient, and the probability of a device failing equals the non-urgent coefficient. The non-urgent coefficient of the voting system is the probability that the system cannot function normally, equal to the sum of the probabilities corresponding to the case where the number of normally functioning units is less than the number of votes. Correspondingly, the overall urgent coefficient of the voting system can be obtained by subtracting this non-urgent coefficient from a constant, reflecting the system's ability to function normally. When the non-urgent coefficients of the devices constituting the voting system are not the same, meaning different devices have different failure risk levels, a method based on enumerating all possible states is needed to determine the non-urgent coefficient of the voting system. Specifically, all possible combinations of normal operating states of all devices in the voting system are listed. For each combination, the number of devices operating normally under that combination is determined. If this number is less than the number of votes, the state corresponding to that combination is a system failure state, and the probability of this combination occurring is equal to the product of the normal operating probability of each device in the combination and the failure probability of the non-normal operating devices. Adding the probabilities corresponding to all combinations of system failure yields the non-urgent coefficient of the voting system. Correspondingly, the overall urgent coefficient of the voting system can be obtained by subtracting this non-urgent coefficient from a constant, reflecting the system's ability to operate normally. Finally, the overall risk index of the entire target system is determined based on the comprehensive urgency coefficient of the voting system. In this application, the overall risk index of the voting system can be determined in the following manner:

[0045]

[0046] In the formula, This is the emergency coefficient of the voting system (i.e., the comprehensive emergency system). The urgency coefficients of each device in the target system. This is a comprehensive risk index for the voting system. For combinations, For the summation variable.

[0047] The risk calculation method described above for the voting system can accurately calculate the overall risk level of the system based on the redundancy relationship between devices. This allows the calculated comprehensive risk index to truly reflect the system's fault tolerance capability to maintain normal operation even when some devices fail, providing an accurate and reliable basis for quantifying risks in the subsequent development of scientific and reasonable maintenance strategies.

[0048] In this embodiment, when the number of votes is equal to the total number of units (m=n), the voting system is treated as a serial system, and the overall urgency coefficient of the voting system is determined based on the serial system. Specifically, the overall urgency coefficient of the serial system is determined based on the urgency coefficient of each device, and the overall risk index is determined based on the overall urgency coefficient of the serial system.

[0049]

[0050] In the formula, This is the emergency factor for a series system (i.e., the combined emergency system). The urgency coefficients of each device in the target system. The comprehensive risk index for a series system; When the number of votes is 1 (m=1), the voting system is treated as a parallel system, and the overall urgency coefficient of the voting system is determined based on the parallel system. Specifically, the overall urgency coefficient of the parallel system is determined based on the urgency coefficient of each device, and the overall risk index is determined based on the overall urgency coefficient of the parallel system.

[0051]

[0052] In the formula, This is the emergency coefficient for the parallel system (i.e., the comprehensive emergency system). This is the comprehensive risk index for parallel systems.

[0053] The risk assessment method for thermal power equipment with redundant systems provided in this application, for the specific connection relationship of the voting system, introduces the concepts of voting number and total number of units, and combines the emergency coefficient of each device to comprehensively calculate the overall risk of the system. This can truly reflect the actual risk level of the voting system in the case of partial equipment failure, avoiding the problem of overestimation or underestimation of risk due to simplified processing, and accurately handling the impact of individual equipment differences on the overall risk of the system. This makes the risk assessment results more consistent with the actual operating conditions of the equipment, improving the accuracy of the assessment. At the same time, based on the quantitative analysis of system risk, limited maintenance resources are prioritized for the equipment that contributes the most to the system risk, improving resource utilization efficiency and reducing maintenance costs.

[0054] This application provides a risk assessment system for thermal power equipment with redundant systems, see below. Figure 3 As shown, the risk assessment system for thermal power equipment with redundant systems provided in this application includes: Data acquisition module 210 is used to acquire characteristic parameters of each device in the target system; the characteristic parameters include system connection relationship, device importance parameter, device health status parameter and device real-time reliability parameter; the system connection relationship includes voting system; The risk assessment module 220 is used to determine the urgency coefficient of each device based on its importance parameters, health status parameters, and real-time reliability parameters; to determine the comprehensive urgency coefficient of the voting system based on the number of votes and the total number of units, as well as the urgency coefficient of each device; and to determine the comprehensive risk index based on the comprehensive urgency coefficient of the voting system.

[0055] It should be noted that the principle of the risk assessment system for thermal power equipment with redundant systems provided in this application embodiment is similar to the risk assessment method for thermal power equipment with redundant systems provided in this application embodiment. Therefore, the implementation of the risk assessment system for thermal power equipment with redundant systems provided in this application embodiment can refer to the implementation of the risk assessment method for thermal power equipment with redundant systems provided in this application embodiment, and the repeated parts will not be described again.

[0056] After introducing the risk assessment method and apparatus for thermal power equipment with redundant systems provided in the embodiments of this application, the electronic equipment provided in the embodiments of this application will be briefly introduced next.

[0057] See Figure 4 As shown, the electronic device 500 provided in this application embodiment includes at least a processor 501, a memory 502, and a computer program stored in the memory 502 and executable on the processor 501. When the processor 501 executes the computer program, it implements the risk assessment method for thermal power equipment with a redundant system provided in this application embodiment.

[0058] The electronic device 500 provided in this application embodiment may further include a bus 503 connecting different components (including processor 501 and memory 502). The bus 503 represents one or more types of bus structures, including memory bus, peripheral bus, local area bus, etc.

[0059] Memory 502 may include a readable storage medium in the form of volatile memory, such as random access memory (RAM) 5021 and / or cache memory 5022, and may further include read-only memory (ROM) 5023. Memory 502 may also include a program tool 5025 having a set (at least one) of program modules 5024, including but not limited to an operating subsystem, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0060] Processor 501 can be a single processing element or a collective term for multiple processing elements. For example, processor 501 can be a central processing unit (CPU) or one or more integrated circuits configured to implement the risk assessment method for thermal power equipment with redundant systems provided in the embodiments of this application. Specifically, processor 501 can be a general-purpose processor, including but not limited to CPUs, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0061] Electronic device 500 can communicate with one or more external devices 504 (e.g., keyboard, remote control, etc.), and also with one or more devices that enable a user to interact with electronic device 500 (e.g., mobile phone, computer, etc.), and / or with devices that enable electronic device 500 to communicate with one or more other electronic devices 500 (e.g., router, modem, etc.). This communication can be performed through input / output (I / O) interface 505. Furthermore, electronic device 500 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) through network adapter 506. Figure 4As shown, network adapter 506 communicates with other modules of electronic device 500 via bus 503. It should be understood that, although... Figure 4 As not shown, other hardware and / or software modules may be used in conjunction with the electronic device 500, including but not limited to microcode, device drivers, redundant processors, external disk drive arrays, Redundant Arrays of Independent Disks (RAID) subsystems, tape drives, and data backup storage subsystems.

[0062] It should be noted that, Figure 4 The electronic device 500 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0063] The following describes the computer-readable storage medium provided in the embodiments of this application. The computer-readable storage medium provided in the embodiments of this application stores computer instructions, which, when executed by a processor, implement the risk assessment method for thermal power equipment with redundant systems provided in the embodiments of this application. Specifically, the computer instructions can be built into or installed in the processor, so that the processor can implement the risk assessment method for thermal power equipment with redundant systems provided in the embodiments of this application by executing the built-in or installed computer instructions.

[0064] In addition, the risk assessment method for thermal power equipment with redundant systems provided in this application embodiment can also be implemented as a computer program product. The computer program product includes program code, which implements the risk assessment method for thermal power equipment with redundant systems provided in this application embodiment when running on a processor.

[0065] The computer program product provided in this application embodiment may employ one or more computer-readable storage media, which may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination thereof. Specifically, more specific examples (a non-exhaustive list) of computer-readable storage media include electrical connections with one or more wires, portable disks, hard disks, RAM, ROM, erasable programmable read-only memory (EPROM), optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0066] The computer program product provided in this application embodiment can be a CD-ROM and include program code, and can also run on electronic devices such as computers. However, the computer program product provided in this application embodiment is not limited thereto. In this application embodiment, the computer-readable storage medium can be any tangible medium that contains or stores program code, which can be used by or in conjunction with an instruction execution system, device, or apparatus.

[0067] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0068] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0069] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0070] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A risk assessment method for thermal power equipment with redundant systems, characterized in that, include: Obtain the characteristic parameters of each device in the target system; The characteristic parameters include system connectivity parameters, device importance parameters, device health status parameters, and device real-time reliability parameters; the system connectivity parameters include a voting system. The urgency coefficient of each device is determined based on its importance parameter, health status parameter, and real-time reliability parameter. Based on the number of votes and the total number of units in the voting system, as well as the urgency coefficient of each device, the overall urgency coefficient of the voting system is determined. Based on the comprehensive urgency coefficient of the voting system, a comprehensive risk index is determined.

2. The risk assessment method for thermal power equipment with redundant systems according to claim 1, characterized in that, Determining the overall urgency coefficient of the voting system includes: Based on the urgency coefficient of each device in the voting system, the non-urgency coefficient of each device is determined. The non-urgent coefficient of the voting system is determined based on the number of votes in the voting system, the total number of units, and the non-urgent coefficient of each device. The overall urgency coefficient of the voting system is determined based on the non-urgent coefficient of the voting system.

3. The risk assessment method for thermal power equipment with redundant systems according to claim 2, characterized in that, Determining the non-urgent coefficient of the voting system includes: When the non-urgent coefficients of each device in the voting system are the same, the non-urgent coefficient of the voting system is determined based on the combination coefficients corresponding to each value within the range of the number of votes to the total number of units and the non-urgent coefficients of each device. When the non-urgent coefficients of each device in the voting system are different, the non-urgent coefficients of the voting system are determined based on different combinations of normal operating states of each device and their corresponding non-urgent coefficients.

4. The risk assessment method for thermal power equipment with redundant systems according to claim 1, characterized in that, Also includes: When the number of votes is equal to the total number of units, the voting system is treated as a series system, and the comprehensive urgency coefficient of the voting system is determined by the series system. When the number of votes is 1, the voting system is treated as a parallel system, and the overall urgency coefficient of the voting system is determined by the parallel system.

5. The risk assessment method for thermal power equipment with redundant systems according to claim 4, characterized in that, Also includes: When the voting system is used as the series system, the comprehensive emergency coefficient of the series system is determined based on the emergency coefficient of each device, and the comprehensive risk index is determined based on the comprehensive emergency coefficient of the series system. When the voting system is used as a parallel system, the comprehensive emergency coefficient of the parallel system is determined based on the emergency coefficient of each device, and the comprehensive risk index is determined based on the comprehensive emergency coefficient of the parallel system.

6. The risk assessment method for thermal power equipment with redundant systems according to claim 1, characterized in that, Determine the equipment importance parameters for each of the aforementioned devices, including: Determine multiple assessment metrics for each device to evaluate its importance; The weight coefficient for each evaluation indicator is determined based on the multiple evaluation indicators. The importance parameter of each device is determined based on the multiple evaluation indicators and multiple weighting coefficients.

7. The risk assessment method for thermal power equipment with redundant systems according to claim 1, characterized in that, Determine the device health status parameters for each of the aforementioned devices, including: Determine the equipment type for each of the aforementioned devices, including rotating machinery and non-rotating machinery; For the rotating machinery, the equipment status is determined based on the vibration parameters of the rotating machinery, and the equipment health status parameters are determined based on the equipment status. For the non-rotating mechanical equipment, control limits are determined based on the moving average value and moving range of the process parameter measurement points of the non-rotating mechanical equipment. The equipment status of the non-rotating mechanical equipment is determined according to the control limits, and the equipment health status parameters are determined according to the equipment status.

8. A risk assessment system for thermal power equipment with redundancy, characterized in that, include: The data acquisition module is used to acquire the characteristic parameters of each device in the target system; The characteristic parameters include system connectivity parameters, device importance parameters, device health status parameters, and device real-time reliability parameters; the system connectivity parameters include a voting system. The risk assessment module is used to determine the urgency coefficient of each device based on the device importance parameter, device health status parameter, and device real-time reliability parameter. Based on the number of votes and the total number of units in the voting system, as well as the urgency coefficient of each device, the overall urgency coefficient of the voting system is determined. Based on the comprehensive urgency coefficient of the voting system, a comprehensive risk index is determined.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the risk assessment method for thermal power equipment with redundant systems as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the risk assessment method for thermal power equipment with redundant systems as described in any one of claims 1 to 7.

Images