KR102603557B1 - System and Method of Hybrid Reliability Centered Maintenance for Power Generation Facilities - Google Patents

Abstract

Introducing a simplified reliability-centered maintenance (Streamlined RCM) analysis that enables efficient analysis using the Preventive Maintenance Basis Database (PMBD), such as a failure library, and using a waveform distribution for decision-making. A retrospective reliability-centered maintenance system including cost effectiveness that can increase the accuracy of parts replacement cycle and/or preventive maintenance by analyzing reliability is disclosed. The retrospective reliability-centered maintenance system includes a collection unit that collects required data for preventive maintenance information for power generation facilities from a database, a simplified reliability-centered maintenance (streamlined RCM (Reliability-Centered Maintenance)) analysis and A calculation module that performs standard reliability-centered maintenance (Standard RCM) analysis to calculate the preventive maintenance information for multiple components of the power generation facility, and a cost-effectiveness function calculated through the preventive maintenance information to determine cost effectiveness. It is characterized by including a decision-making module that performs degree-based decision-making.

Description

{System and Method of Hybrid Reliability Centered Maintenance for Power Generation Facilities}

The present invention relates to retrospective reliability-centered maintenance (Hybrid RCM (Reliability Centered Maintenance)) technology including cost effectiveness, and in particular, costs such as the time required for RCM analysis, quantitative replacement cost, and operating cost while maintaining a high operation rate of power generation facilities. This is about a simplified retrospective reliability-centered maintenance system and method to reduce and improve reliability and efficiency.

In addition, the present invention relates to a cost-effective retrospective reliability-oriented maintenance system and method that prevents maintenance omissions and improves maintenance accuracy.

Recently, system failure diagnosis and prognosis technology (PHM: Prognostic and Health Management), which monitors the status of industrial equipment using sensors to diagnose failures and predict the remaining useful life, is being actively applied in all industrial fields.

This failure diagnosis and prediction technology (PHM) uses sensors to monitor, diagnose abnormal conditions, predict when a failure level will be reached (prognostics), and take action when necessary (decision making). It is a technique that does. In particular, power plants are composed of a combination of various power generation facilities and are equipped with various driving facilities such as multiple gas turbines, pumps, fans, etc., which generate mechanical vibration, so this is a very necessary technology field.

If any one of the various facilities in a power plant breaks down, it will have a significant impact on the reliability and economic efficiency of the power plant, so preventive maintenance is performed before a breakdown occurs in each facility.

The purpose of preventive maintenance performed at power plants is to improve the safety and operation rate of power plants by predicting, preventing, or minimizing failure of major facilities. Reliability-Centered Maintenance (RCM) is widely used as a method to determine the parts subject to preventive maintenance among the many parts or devices installed in a power plant and the appropriate maintenance method and cycle.

However, RCM has complex procedures and requires experts with knowledge and experience to participate as a team to perform optimized preventive maintenance. Since strategy establishment for actual asset management decisions plays an important role, it costs a lot of time and money. There is a problem.

In addition, because normal RCM analysis places a heavy workload on the person in charge, many people avoid RCM analysis work in the actual field, resulting in frequent personnel transfers and few experts with professional RCM analysis skills. In addition, the period of operation without failure is relatively long, making it difficult to apply general or standard RCM analysis methods to power plants.

In particular, one example of using the RCM method is the reliability-centered maintenance management method of power generation facilities shown in Korea Patent Publication No. 10-1341231 (registration date: 2013.12.06). The technology includes a step of the computer determining a system subject to reliability analysis to prevent failure from the operation information and maintenance information, a step of the computer assigning functions to the determined system subject to reliability analysis and assigning functional failures, and a step of the computer determining the system to be subject to reliability analysis to prevent failures. A step of allocating all equipment from the equipment list information for each failure and analyzing the impact of functional failure on each equipment; a step of computer determining preventive inspection and maintenance tasks to prevent functional failure; and a computer determining mean time between failures (MTBF). :Mean Time Between Failure) data is input and the preventive inspection and maintenance cycle is determined through waveform distribution analysis, and the preventive inspection and maintenance work determined by the computer is compared with the existing preventive inspection and maintenance standards to determine optimal preventive inspection and maintenance. It includes the step of determining maintenance standards.

However, not only does it require the participation of experts to analyze and make decisions at each stage, resulting in a lot of cost and time, but it also has the problem of inefficiency because the decision is based only on the basic equipment history and predicted failure rate created at the time of design. there is.

Another example is the power generation facility management system and its control method shown in Korean Patent Publication No. 10-1329395 (registration date: 2013.11.07). The above technology also standardizes maintenance work procedures, allowing maintenance work to be managed using the same procedure for all maintenance work, and enables power generation operation management to be monitored through the real-time plant information system (PIS: Plant Information System) and tag numbers. A technology that can perform reliability analysis and risk assessment based on real-time driving information is presented. However, there is still no specific method on how to establish a maintenance plan.

If existing RCM analysis methods are appropriately utilized in power plants, optimal preventive maintenance with high reliability can be performed, but it takes a lot of time and human resources to analyze failures and match maintenance tasks for each component or device installed in many power generation facilities. Therefore, there is a problem of low usability in the field. In addition, when applied to the facilities of the entire power plant, too much data must be handled, so there are cases where partial facility changes cannot be properly reflected.

In addition, most systems used in existing maintenance strategies are characterized by design and quality control using a failure rate function. These existing methods for determining the replacement cycle of parts reflect the designer's experience and the manufacturer's recommended replacement cycle at the design stage, resulting in failure to predict the actual replacement cycle. Parts or equipment are replaced despite the remaining life, or the operating environment is not designed according to the design conditions. If it is too different, premature failure may occur, resulting in reduced productivity.

In addition, existing methods mainly analyze the lifespan of parts or equipment, and have limitations in calculating maintenance activities such as operation and maintenance of equipment as costs, which limits managers' ability to make smooth decisions.

1. Korean Patent Publication No. 10-1341231 (registration date: 2013.12.06) 2. Korean Patent Publication No. 10-1329395 (registration date: 2013.11.07)

The present invention was proposed to solve the problems caused by the above background technology, and is a simplified reliability-centered maintenance configured to enable efficient analysis using a preventive maintenance basis database (PMBD) such as a failure library. Retrospective reliability-centered maintenance system and method including cost effectiveness that can increase the accuracy of parts replacement cycle and/or preventive maintenance by analyzing reliability using waveform distribution for decision making along with introduction of (Streamlined RCM) analysis. The purpose is to provide.

In addition, in order to increase reliability, the present invention calculates life cycle cost and availability through maintenance history and maintenance computer information, compares and reviews system effectiveness, and analyzes reliability according to cost effectiveness to make clearer and more accurate decisions. Another purpose is to provide a retrospective reliability-oriented maintenance system and method including cost-effectiveness.

In order to achieve the task presented above, the present invention provides a simplified reliability-centered maintenance (Streamlined RCM) analysis configured to enable efficient analysis by utilizing a preventive maintenance basis database (PMBD) such as a failure library. With its introduction, it provides a retrospective reliability-centered maintenance system including cost effectiveness that can increase the accuracy of parts replacement cycle and/or preventive maintenance by analyzing reliability using waveform distribution for decision making.

The retrospective reliability-centered maintenance system is,

a collection unit that collects required data for preventive maintenance information on power generation equipment from a database;

Using the above required data, streamlined reliability-centered maintenance (RCM) analysis and standard reliability-centered maintenance (Standard RCM) analysis are performed to calculate the preventive maintenance information for multiple components of the power generation equipment. a calculation module; and

and a decision-making module that performs cost-effectiveness-based decision-making using a cost-effectiveness function calculated through the preventive maintenance information.

In addition, the database is characterized as a preventive maintenance basis database (PMBD) for the power generation equipment.

In addition, the Standard Reliability-Centered Maintenance (Streamlined RCM) analysis is characterized in that it is performed only on some of the components for which analysis is desired among the plurality of components that are omitted in the Streamlined Reliability-Centered Maintenance (Streamlined RCM) analysis. Do it as

At this time, the number of missing components is characterized in that it is determined whether or not to be fixed by setting the standard of the Asset Health Indicator (AHI).

Meanwhile, the asset health index (AHI) sets the lower limit standard of the normal state, and when the lower limit standard is reached, the corresponding component is recognized as a failure, and the point of reaching is the MTFF (Mean Time to First Failure) of the corresponding component. ) is characterized in that it is determined.

In addition, for the component parts that do not reach the lower limit standard of the asset health index (AHI), a probability distribution parameter based on a pre-set literature search is applied to calculate a failure rate function over time, and the failure rate function is used to calculate the failure rate function. It is characterized by calculating the preventive maintenance cycle of the corresponding component parts.

In addition, the simplified reliability-centered maintenance (Streamlined RCM) analysis is characterized as one of the passive approaches, general analysis and list usage, and process element omission and partial analysis methods.

In addition, the decision-making module calculates a cost-effectiveness function for decision-making based on cost-effectiveness, and the cost-effectiveness function is calculated by dividing the availability function delivered from the computerized maintenance management system by the cost function among the preventive maintenance information. It is characterized by being defined as.

In addition, the decision-making module is characterized by using a probability density function calculated using a key parameter fitting method among statistical calculations through wavele distribution theory to determine the reliability of the corresponding component applied to the cost-effectiveness function.

In addition, the decision is characterized in that it is output as a graph of the total cost-replacement cycle function.

In addition, the cost-effectiveness-based decision-making is characterized by using the life cycle cost reviewed in the life cycle cost review item and the system effectiveness reviewed in the system effectiveness review item.

On the other hand, another embodiment of the present invention includes the steps of: (a) a collection unit collecting required data for preventive maintenance information for power generation facilities from a database; (b) The calculation module utilizes the required data to perform Streamlined Reliability-Centered Maintenance (RCM) analysis and Standard Reliability-Centered Maintenance (Standard RCM) analysis on multiple components of the power generation facility. calculating the preventive maintenance information; and (c) a decision-making module performing cost-effectiveness-based decision-making using a cost-effectiveness function calculated through the preventive maintenance information. Retrospective reliability-based maintenance including cost-effectiveness. Provides a method.

According to the present invention, by quantifying the failure of equipment or parts of a power plant using the asset health index standard, not only can the accuracy of maintenance be significantly improved, but also the reliability of failure data for power generation equipment can be significantly improved.

In addition, another effect of the present invention is to derive cost-effectiveness by simultaneously analyzing life cycle cost, availability, and reliability through wavelet distribution theory for decision making and to determine the optimal cycle using Generic Algorithm (GA). This provides the advantage of being able to perform preventive maintenance accurately and at optimal cost.

In addition, another effect of the present invention is to actively utilize the Preventive Maintenance Basis Database (PMBD) of power generation facilities to supplement missing maintenance or select only the facilities to be analyzed to perform RCM (Reliability Centered Maintenance) analysis. Since it is possible to perform this method, it has the advantage of saving time and cost compared to the existing RCM method.

In addition, another effect of the present invention is that the preventive maintenance cycle for each facility can be efficiently determined, thereby significantly improving the availability and cost-effectiveness of power generation facilities.

Figure 1 is a block diagram of a retrospective reliability-centered maintenance system including cost effectiveness according to an embodiment of the present invention.
FIG. 2 is a diagram showing the relationship between the calculation module and decision-making module shown in FIG. 1.
Figure 3 is an example of an objective function according to an embodiment of the present invention.
Figure 4 is a flowchart showing a retrospective reliability-centered maintenance process including cost effectiveness according to an embodiment of the present invention.
Figure 5 is a conceptual diagram of calculating cost effectiveness using life cycle cost review items and system effectiveness review items according to an embodiment of the present invention.
Figure 6 is a graph showing the relationship between total replacement cost and replacement cycle according to an embodiment of the present invention.
Figure 7 is a table showing the maintenance status comparison of the pump according to an embodiment of the present invention.

Since the present invention can be modified in various ways and can have various embodiments, specific embodiments will be illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to specific embodiments, and should be understood to include all changes, equivalents, and substitutes included in the spirit and technical scope of the present invention.

When describing each drawing, similar reference numerals are used for similar components. Terms such as first, second, etc. may be used to describe various components, but the components should not be limited by the terms. The above terms are used only for the purpose of distinguishing one component from another.

For example, a first component may be referred to as a second component, and similarly, the second component may be referred to as a first component without departing from the scope of the present invention. The term “and/or” includes any of a plurality of related stated items or a combination of a plurality of related stated items.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by a person of ordinary skill in the technical field to which the present invention pertains.

Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and unless clearly defined in the present application, should not be interpreted in an ideal or excessively formal sense. It shouldn't be.

Hereinafter, with reference to the attached drawings, a retrospective reliability-centered maintenance system and method including cost effectiveness according to an embodiment of the present invention will be described in detail.

Retrospective reliability centered maintenance (Hybrid RCM (Reliability Centered Maintenance)) technology including cost effectiveness according to an embodiment of the present invention is a maintenance optimization strategy that links cost effectiveness with maintaining a high operation rate of power generation facilities, and is a preventive maintenance standard database (PMBD: Preventive Maintenance Basis Database) is used. Meanwhile, standard RCM analysis is performed only on parts or devices to be analyzed among those missing from streamlined RCM. Additionally, by including reliability and cost-effectiveness, optimal maintenance technology can be implemented that can significantly improve efficiency and accuracy.

In addition, a step is implemented to select power generation facilities that are missing from streamlined reliability-centered maintenance (streamlined RCM) and/or facilities that require more accurate analysis and perform standard RCM analysis on them. In addition, through quantitative data acquisition and management, the maintenance cycle centered on design values that are different from reality has been improved, and key parameter fitting (shape parameters, scale parameters) during statistical calculations through wavelet distribution theory is used to calculate facility reliability. etc.) is calculated to calculate the probability density function (failure rate), and a graph of the total cost-replacement cycle function is created in the calculation of the optimal cycle through the quantitative replacement cost, operating cost, reliability, etc. A step is added to calculate the optimal result by outputting the optimal replacement cycle result and using a genetic algorithm.

Meanwhile, in one embodiment of the present invention, failure is defined using the Asset Health Indicator (AHI) in order to present an accurate quantitative method in detecting failure of a power plant.

In addition, the optimal preventive maintenance cycle can be determined by determining the replacement cycle corresponding to the lowest value of the total replacement cost by additionally utilizing a decision-making process based on the cost-effectiveness function along with the availability of power generation facilities. .

At this time, to calculate the total replacement cost, the Weibull statistical method is used to utilize failure rate and/or maintenance history information and life cycle cost information (LCC), and when a multivariate optimal point exists, a genetic algorithm (GA, Generic It may include a machine learning process to find the optimal solution using an algorithm.

Figure 1 is a block diagram of a retrospective reliability-centered maintenance system 100 including cost effectiveness according to an embodiment of the present invention. Referring to FIG. 1, the retrospective reliability-centered maintenance system 100 includes a collection unit 110 that collects required data from the database 10, streamlined reliability-centered maintenance (Streamlined RCM) analysis and standard reliability using the required data. A calculation module 120 that performs standard RCM analysis, a decision module 130 that performs cost-effectiveness-based decision-making using the values calculated through analysis, and a re-evaluation module 140 that reevaluates the decision. ), and an output unit 150 that outputs a re-evaluation result.

The collection unit 110 performs the function of collecting required data from the database 10. The database 10 may be a preventive maintenance basis database (PMBD) for power generation facilities. This preventive maintenance criteria database could be the PMBD created by the Electric Power Research Institute (EPRI). Usually, it is necessary to perform maintenance before the probability of device failure increases again to prove that the target device is healthy and has stable performance. This activity is generally referred to as preventive maintenance. Therefore, the Preventive Maintenance Basis Database (PMBD) provides the basis for performing such preventive maintenance.

General preventive maintenance is condition-based maintenance, in which maintenance is performed according to the condition of the device. An example of the implementation of the Preventive Maintenance Basis Database (PMBD) is the paper by Byeong-hak Lee et al., "Development of preventive maintenance standards for nuclear power plant steam turbines based on EPRI's preventive maintenance basic data" (2010). Since this preventive maintenance standard database is widely known, further explanation will be omitted.

The database 10 may be implemented within the retrospective reliability-centered maintenance system 100, or may be connected to an external server (not shown) on which the database 10 is built through a communication network. Additionally, the database 10 may be configured as a separate database server within the retrospective reliability-centered maintenance system 100.

A communication network refers to a connection structure that allows information exchange between nodes such as multiple terminals and servers, such as public switched telephone network (PSTN), public switched data network (PSDN), and integrated services digital network (ISDN). Networks), Broadband ISDN (BISDN), Local Area Network (LAN), Metropolitan Area Network (MAN), Wide LAN (WLAN), etc. However, , the present invention is not limited thereto, and includes wireless communication networks such as CDMA (Code Division Multiple Access), WCDMA (Wideband Code Division Multiple Access), Wibro (Wireless Broadband), WiFi (Wireless Fidelity), and HSDPA (High Speed Downlink Packet Access). Network, Bluetooth, NFC (Near Field Communication) network, satellite broadcasting network, analog broadcasting network, DMB (Digital Multimedia Broadcasting) network, etc. Alternatively, it may be a combination of these wired communication networks and wireless communication networks.

Continuing to refer to Figure 1, the calculation module 120 performs the function of performing Streamlined RCM analysis and Standard RCM analysis. Streamlined Reliability-Centered Maintenance Analysis (Streamlined RCM) generates preventive maintenance information for components in power generation equipment. Preventive maintenance information includes failure rates, reliability, and statistical indicators. Statistical indicators include shape parameters and scale parameters. The shape parameter is a factor that explains how the data is distributed, and the scale parameter is a factor that defines the position relative to the threshold of the Weibull curve.

Standard RCM analysis is performed only on the components to be analyzed among those missing in Streamlined RCM. A component part is a concept that includes component devices. In other words, a device may be a component used in a power generation facility, and a component may be a part of the device.

Component parts include ① Diffusers, Volutes & Channel Rings, ② Balancing Device, ③ Wear Rings and surfaces, ④ Shaft, ⑤ Bearing Oil Slinger. Ring), ⑥ Bearing Lubricant (Bearings-Antifriction), and ⑦ Bearing Sleeve. A table showing this in more detail is shown in Figure 7. Figure 7 will be described later.

Continuing to refer to Figure 1, there are three methods of streamlined RCM analysis: a passive approach, general analysis and list usage, process element omission, and partial analysis.

First, Retroactive Approaches are methods that start the process from maintenance work rather than defining functions like standard RCM. However, the existing maintenance management program reduces stability by assuming that it includes all failure modes that require preventive maintenance, and reduces accuracy because it cannot derive an accurate failure mode because function definition is not preceded. occurs. For this reason, there is a problem that the risk that more failures may occur than the analysis results increases.

Second, general analysis and list usage is a method in which technically identical systems use analysis performed in one system together, and are applied to systems that wish to analyze a general-purpose Failure Mode List or FMEA List created by a third party. . In most cases, general analysis and list usage include only specific individual assets or single components, so the list may not include the entire system, and in this case, there is a problem of reduced accuracy.

Third, the omitted process element and partial analysis method omits some of the process elements of Standard RCM, generally omits the function definition step, omits the process element that lists failure modes, and omits functions and equipment that are deemed important or critical. It is a partial analysis method that analyzes only failures with expected results. This partial analysis method requires technical definition of function and confirmation of performance to make it easy to identify the failure mode. However, if an accurate failure mode is not derived, there is a problem that it may result in deterioration of process performance.

The system based on the three application methods above analyzes the existing maintenance status through deterioration triggering and uses PMBD to supplement missing maintenance or perform retrospective RCM analysis to select only the equipment to be analyzed. However, since this analysis is also based on the failure rate of the equipment, there is no reliability analysis of parts and equipment of power generation equipment. In addition, problems may arise in the actual maintenance strategy as the decision is made without including information related to costs or budgets that have occurred or will occur in the future.

Therefore, in one embodiment of the present invention, standard RCM analysis is performed on the component parts to be analyzed among the components missing in streamlined reliability-centered maintenance (Streamlined RCM).

Standard RCM analysis defines failures using the Asset Health Indicator (AHI) standards of the power generation equipment and determines the preventive maintenance cycle using the availability of the power generation equipment.

The decision-making module 130 performs a decision-making function including cost effectiveness. In other words, the timing of replacement and maintenance of components is determined through cost effectiveness. Of course, for this purpose, the total cost and total availability of the power generation equipment or component parts are used, and the replacement cycle corresponding to the lowest value of the total replacement cost is determined.

The re-evaluation module 140 performs the function of re-evaluating the decision made by the decision-making module 130. The decision-making module 130 provides preventive maintenance-related results through a simplified reliability-centered maintenance (Streamlined RCM) analysis method. Since it is derived, there may be parts missing from the decision-making module 130 among the parts shown to not require actual preventive maintenance.

Therefore, standard RCM analysis is performed through the re-evaluation module 140 on parts or devices that are not designated as preventive maintenance items in the decision-making module 130. Through the re-evaluation module 140, some of the parts or devices shown as not requiring preventive maintenance in the decision-making module 130 may be confirmed as requiring maintenance. In addition, by performing standard RCM through the re-evaluation module 140 after analyzing streamlined RCM through the decision-making module 130, the RCM analysis technique can be efficiently utilized while appropriate maintenance can be performed without omission.

The output unit 150 performs the function of outputting processed data or providing a settings screen, menu screen, etc. In addition, it performs a function of outputting the results processed by the decision-making module 130 and the re-evaluation module 140 in a combination of voice, graphics, and text. For this purpose, the output unit 150 is configured with a display, a sound system, etc. Displays include LCD (Liquid Crystal Display), LED (Light Emitting Diode) display, PDP (Plasma Display Panel), OLED (Organic LED) display, touch screen, CRT (Cathode Ray Tube), flexible display, micro LED, mini LED, etc. This can be. In the case of a touch screen, it can also be used as an input means to input user commands.

Additionally, the terms “collection unit” and “~module” shown in FIG. 1 refer to a unit that processes at least one function or operation, which may be implemented in software and/or hardware. In hardware implementation, an application specific integrated circuit (ASIC), digital signal processing (DSP), programmable logic device (PLD), field programmable gate array (FPGA), processor, microprocessor, and other devices designed to perform the above-described functions. It may be implemented as an electronic unit or a combination thereof.

In software implementation, software composition components (elements), object-oriented software composition components, class composition components and task composition components, processes, functions, properties, procedures, subroutines, segments of program code, drivers, firmware, microcode, and data. , databases, data structures, tables, arrays, and variables. Software, data, etc. can be stored in memory and executed by a processor. The memory or processor may employ various means well known to those skilled in the art.

FIG. 2 is a diagram showing the relationship between the calculation module 120 and the decision-making module 130 shown in FIG. 1. Referring to FIG. 2, the decision-making module 130 performs decision-making using a cost-effectiveness function (E(t _p )). For this purpose, the cost function (C(t _p )) is received from the calculation module 120, and the availability function (A(t _p )) is received from the computerized maintenance management system 210. Therefore, the cost-effectiveness function (E(t _p )) can be expressed as the following equation.

The computerized maintenance management system 210 is composed of equipment master/work order information including equipment number, failure history, maintenance history, introduction price, installation cost, and trial run cost.

The availability function (A(t _p )) is It is defined as Here, R(t _p ) is the reliability function. It is defined as , and F(t _p ) is the time density function until failure. It is defined as Also, here β is the shape parameter, is the scale parameter, is the location parameter, and t is the time variable.

Additionally, M(t _p ) is MTBF, It is defined as Here, T _i is inspection time, T _r is repair time, and T _p is preventive maintenance time.

The cost function (C(t _p )) is It is defined as Here, C _p is the preventive maintenance cost and C _f is the breakdown cost.

The calculation module 130 transmits failure rate, reliability, statistical indicators, etc. calculated using a preventive maintenance basis database (PMBD) to the decision-making module 130. Facility reliability calculations include statistical indicators such as shape parameters and scale parameters to include reliability in addition to the failure rate, and failure history/maintenance history can be recalled. Accordingly, the value calculated from the information of the computerized maintenance management system 210 and the reliability calculation of the power generation equipment is finally determined in the cost-effectiveness-based decision-making module 140.

Figure 3 is an example of an objective function according to an embodiment of the present invention. Referring to FIG. 3, the decision-making module 140 includes the calculated failure rate value, decision variable, and maintenance history calculated above in order to build a more accurate maintenance strategy system by including the cost-effectiveness function (E(t _p )). , reliability, and life cycle cost (LCC: Life Cycle Cost) information, etc. are additionally secured and used to increase the accuracy of the system. The objective function shown in Figure 3 is used.

To elaborate, the cost function cannot be 0, so when the availability function becomes 0, the cost effectiveness can be 0. Therefore, for the availability function to be 0, is 0 and MTBF is 0. In other words, it represents a case where a faulty state is maintained when performing a cost-effectiveness analysis.

In order to apply the above reliability, the reliability of power generation equipment is calculated by calculating shape parameters and scale parameters through key parameter fitting during statistical calculations using wavelet distribution theory. Afterwards, the probability density function (failure rate) is calculated using the shape parameter and scale parameter. Quantitative replacement cost and operating cost for the power generation equipment calculated from the results of compiling the quantitative data acquired from the existing computerized maintenance management system 210 and the failure rate obtained above and calculation of the optimal cycle for replacement of parts or power generation equipment. , the decision-making module 130 outputs a graph of the total cost-replacement cycle function through information such as reliability. Afterwards, the decision-making module 130 utilizes a genetic algorithm to determine the optimal replacement cycle result.

As described above, decision-making module 140 determines the optimal cycle for replacement of components or power generation equipment. The cycle decision is made at this time because the highest point of cost-effectiveness is the optimal maintenance time. Therefore, the cost-effectiveness function can be expressed as the following equation.

Here, life cycle cost = f (maintenance history, asset information, reliability probability function), availability = f (operation information, reliability probability function). Equation 1 and Equation 2 are the same method, but Equation 2 is expressed to make it easier to compare Equation 1, and means that it has the maximum cost effectiveness. In order to have the maximum value of cost-effectiveness, life cycle cost (LCC) must have the minimum value and usability must have the maximum value. Therefore, the M(MTBF) value must have the maximum value and the inspection & repair time must have the minimum value. The availability function may include a reliability function for operation information (stop, inspection), and the cost function may include the cost incurred when there is no life cycle cost (LCC).

Figure 4 is a flowchart showing a retrospective reliability-centered maintenance process including cost effectiveness according to an embodiment of the present invention. Referring to Figure 4, preventive maintenance standard data such as failure library of power generation equipment, etc.

The maintenance status is confirmed through streamlined reliability-centered maintenance (Streamlined RCM) analysis, which is designed to enable efficient analysis using the base (PMBD) (step S410).

Thereafter, preventive maintenance information is calculated through standard RCM analysis for components missing maintenance in the streamlined reliability-centered maintenance (Streamlined RCM) or components to be analyzed (step S420). At this time, in the standard RCM analysis stage for components missing from maintenance, failures are defined and quantified by setting standards for asset health index (AHI) for each component of the target power generation facility.

In other words, the asset health index (AHI) sets the lower limit standard for the steady state, and when the lower limit standard is reached, the corresponding component is recognized as a failure, and the point at which this is reached is referred to as the MTFF (Mean Time to First Failure) of the corresponding component. decide MTFF is the average time until first failure and is applicable to products that cannot be repaired. Meanwhile, for component parts that do not reach the lower limit standard of the asset health index (AHI), a probability distribution parameter based on a literature search conducted in advance is applied to calculate a failure rate function with respect to time, and this is utilized to prevent the component component in question. Calculate the maintenance interval (FFI).

Additionally, the preventive maintenance cycle can be quantified by utilizing the availability rate of power generation facilities.

Afterwards, cost-effectiveness-based decision-making is performed using the cost-effectiveness function calculated through preventive maintenance information (step S430).

To elaborate, key parameter fitting (shape parameters, scale parameters, etc.) is calculated among the statistical calculations through wavele distribution theory to calculate the reliability of power generation facilities by improving the maintenance cycle focusing on design values that are different from reality, and through this, probability density is calculated. The process of calculating a function (failure rate) may be included. This process affects the accuracy of the cost-effectiveness function.

In addition, an analysis process using genetic algorithms may be additionally included to derive optimal replacement cycle results through information such as quantitative replacement cost, operating cost, and reliability of the components of the power generation facility.

Additionally, decisions can be output as a graph of the total cost-replacement cycle function. A diagram showing this is shown in Figure 6. This will be described later.

Figure 5 is a conceptual diagram of calculating the cost effectiveness 550 using the life cycle cost review item 510 and the system effectiveness review item 520 according to an embodiment of the present invention. Referring to Figure 5, cost-effectiveness-based decision-making utilizes the life cycle cost (530) reviewed in the life cycle cost review item and the system effectiveness (540) reviewed in the system effectiveness review item to determine cost effectiveness. You can decide.

Life cycle cost consists of disposal cost, operating cost, acquisition cost including initial cost, and value cost assessed by remaining life. In addition, operating costs include all operating costs as ongoing costs that recur every year, including management costs, non-operational loss costs, maintenance costs, operation costs, and reorganization.

In addition, it includes non-recurring initial costs such as management costs, initial maintenance costs, initial operation costs, equipment purchase costs, research and development costs, etc. All of the above costs are composed of life cycle costs along with residual value according to the remaining life, and the system effects described below. It can also be analyzed in connection with the review items.

In addition, system effectiveness review items include the designed system operation rate including the function, performance, safety, and operability of power generation facilities, supply waiting time including spare parts, repair, and transportation, and average including test, failure detection, diagnosis, and inspection time. System effectiveness can be analyzed by combining downtime and system operation rate by average operation time of MTBF (Mean Time Between Failure) of repair and redundancy, and cost effectiveness can be determined along with the life cycle cost.

Figure 6 is a graph showing the relationship between total replacement cost and replacement cycle according to an embodiment of the present invention. Referring to Figure 6, the vertical axis represents the total replacement cost, and the horizontal axis represents the replacement cycle (Interval between replacement). Graphs 610, 620, 630 of the total cost-replacement cycle function are shown. The graph (610, 620, 630) of the total cost-replacement cycle function is the graph corresponding to the corresponding component.

Figure 7 is a table showing the maintenance status comparison of the pump according to an embodiment of the present invention. Referring to FIG. 7, as a result of selecting all of the above component parts in the analysis of step S420 (see FIG. 4) and performing a standard RCM analysis, as shown in the table above, preventive maintenance is not required in the maintenance status of step S410. It can be seen that some of the Balancing Device, Shaft, and Bearing Seals-Labrynth, which were shown as missing components, appear to require maintenance.

This includes selecting standards for each asset health index (AHI) for multiple balancing devices, shafts, and bearing seals, and the characteristics of the health index that appear while monitoring them. It can be quantified and expressed from actual measured values, including . Therefore, it is possible to efficiently quantify the preventive maintenance cycle of parts requiring maintenance and prevent omission of maintenance.

Additionally, the steps of the method or algorithm described in relation to the embodiments disclosed herein are implemented in the form of program instructions that can be executed through various computer means such as a microprocessor, processor, CPU (Central Processing Unit), etc., and are computer readable. Can be recorded on any available medium. The computer-readable medium may include program (instruction) codes, data files, data structures, etc., singly or in combination.

The program (instruction) code recorded on the medium may be specially designed and constructed for the present invention, or may be known and usable by those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROM, DVD, and Blu-ray, and ROM and RAM. Semiconductor memory elements specially configured to store and execute program (instruction) code, such as RAM), flash memory, etc., may be included.

Here, examples of program (instruction) code include not only machine language code such as that created by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the present invention, and vice versa.

*100: Retrospective reliability-oriented maintenance system
110: Collection department
120: calculation module
130: Decision-making module
140: Re-evaluation module
150: output unit
210: Computerized maintenance management system

Claims (1)

a collection unit 110 that collects required data for preventive maintenance information on power generation facilities from the database 10;
Using the above required data, streamlined reliability-centered maintenance (RCM) analysis and standard reliability-centered maintenance (Standard RCM) analysis are performed to calculate the preventive maintenance information for multiple components of the power generation equipment. a calculation module 120; and
It includes a decision-making module 130 that performs cost-effectiveness-based decision-making using a cost-effectiveness function calculated through the preventive maintenance information,
The decision-making module 130 calculates a cost-effectiveness function (E(t _p )) for decision-making based on the cost-effectiveness, and the cost-effectiveness function (E(t _p )) is a computerized maintenance management system ( It is defined as dividing the availability function (A(t _p )) delivered from 210) by the cost function (C(t _p )) of the preventive maintenance information,
The availability function (A(t _p )) is expressed by the equation Here, R(t _p ) is the reliability function. It is defined as , and F(t _p ) is the time density function until failure. It is defined as , where β is the shape parameter, is the scale parameter, is the location parameter, t is the time variable, and M(t _p ) is the MTBF, It is defined as Here, T _i is the inspection time, T _r is the repair time, and T _p is the preventive maintenance time),
The cost function (C(t _p )) is expressed by the equation (Here, C _p is preventive maintenance cost and C _f is breakdown cost),
The database 10 is a preventive maintenance basis database (PMBD) for the power generation equipment,
The decision-making is a retrospective reliability-centered maintenance system including cost effectiveness, characterized in that the decision is output as a graph (610, 620, 630) of the total cost-replacement cycle function.