JPS63208716A - Device for supporting equipment maintenance control - Google Patents

Abstract

PURPOSE:To easily support the intention determination of equipment maintaining operation by an input device which inputs information regarding equipment maintenance, etc., an arithmetic processor which diagnoses the state of the equipment maintenance with an operator's indication, and an output device. CONSTITUTION:Input information 1 such as equipment and component information and fault and maintenance history information and an operator's request or indication 2 are stored in a storage device 5 through the input device 3 and arithmetic processor 4. The arithmetic processor 4 retrieves and processes statistically the information stored in the storage device 5 and analyzes its reliability. Further, the rank of the state of equipment maintenance is set according to information regarding the reliability and life of equipment and components and information regarding the maintenance easiness and trouble influence degree of the equipment and components obtained by said functions. The information required for the equipment maintenance control which is obtained from this arithmetic processor 4 is displayed on an output device 6.

Description

[Detailed Description of the Invention] [Object of the Invention] (Field of Industrial Application) The present invention relates to the maintenance management of equipment in large plants, and in particular, diagnoses the maintenance status of equipment based on field data, and diagnoses the maintenance status of equipment based on field data. The present invention relates to an equipment maintenance management support device for supporting decision-making in equipment maintenance work.

(Conventional technology) The conventional inspection and maintenance method for equipment and parts uses a preventive maintenance method in which main equipment is inspected and maintained at regular intervals, and other equipment is subject to clear maintenance procedures. A corrective maintenance method (maintenance is performed when a failure or malfunction occurs) with no fixed period is used.

The maintenance cycle of preventive maintenance equipment is based on the design life value set by the manufacturer, the estimated life value obtained from accelerated life tests, etc., or the reliability data collected and organized from these (document values such as failure rate data), etc. It is determined by considering the safety factor.

However, the lifespan of actual equipment and parts is not uniquely or unchangeably determined by usage conditions, environment, etc.

Furthermore, the timing of maintenance for equipment that supports corrective maintenance is often based on the experience or intuition of experts, and a clear judgment is not made.

(Problem to be solved by the invention) In this way, the current maintenance methods for equipment and parts.

These changes in lifespan values are not taken into consideration, and the lifespan values of each device and component are not managed to provide an appropriate maintenance method, so it is difficult to prevent malfunctions of equipment and components even more than the current situation. It is not possible to do so, and in some cases, maintenance is over-maintained due to safety concerns when operating the plant.

There is a potential for large economic losses.

The present invention was made in view of the above circumstances, and reduces the effort and time required to manage plant equipment and parts by predicting and managing the lifespan of plant equipment and parts, and presenting an appropriate maintenance method. At the same time, the objective is to improve the reliability of plant equipment and parts and reduce their maintenance costs and facility costs.

[Structure of the invention]

(Means for Solving the Problems) In order to achieve the above object, the present invention provides information related to equipment maintenance such as component information of various equipment constituting a plant, failure/malfunction history information, and maintenance status determination/ An input device for inputting evaluation criteria, etc., a storage device for storing this information, a function for searching information stored in the storage device, statistical processing, and reliability analysis according to the operator's request instructions, and these functions. The reliability of equipment and parts obtained.

Information on life expectancy and maintenance difficulty of equipment and parts.

This information is obtained from the arithmetic processing device and the arithmetic processing device that has an equipment maintenance status diagnosis function that ranks the equipment maintenance status based on information related to the degree of failure impact.

Information necessary for equipment maintenance management (e.g. similar failure cases,
MT B of 9 equipment/parts including pending issues and failure analysis results
F (Mean Time BetveanFail)
ura).

The device is characterized by an equipment maintenance management support device that is configured with an output device that outputs and displays information such as failure rate, failure distribution pattern, or maintenance guidance that supports commands regarding appropriate maintenance timing and cycles.

(Operation) Since the present invention is configured as described above, it is possible to submit maintenance guidance regarding optimal maintenance based on past failure and maintenance records of plant equipment and parts. Therefore, the labor and time required for maintenance and management of plant equipment and parts can be reduced, and rationalization and efficiency can be achieved.

(Example) Embodiments of the present invention will be described with reference to the drawings.

FIG. 1 is a block diagram of an embodiment of the present invention.

As shown in the figure, input information 1 such as equipment/component information, failure/maintenance history information, etc., and operator requests/instructions 2 are input to an input device [3]. Input information input to the input device 3 is stored in the storage device 5 via the arithmetic processing device 4.

Furthermore, the stored contents of the storage device 5 are calculated by the arithmetic processing device 4 based on an operator's request/instruction 2, and output/displayed on the output device 6.

FIG. 2 is a block diagram especially for explaining the functions of the arithmetic processing unit 4 and the flow of information processing.

Support information for maintenance management is retrieved from information stored in the storage device 5 via the input/output processing function 41 in response to an operator's request/instruction 2. The functions of statistical processing 43, reliability analysis 44, and maintenance status diagnosis 45 provide a series of processing or processing for each function.

Each function will be explained below.

First, the search function 42 searches system 2 equipment of the plant based on the operator's request/instruction 2 from the storage device M5.

Search for information to create tables such as parts lists and parts replacement history tables, and search for data necessary for statistical processing, as well as contents of similar failure cases, lists of maintenance-related concerns, etc. Information is searched and output/displayed from the output device 6.

Next, the statistical processing function 43 organizes and tabulates the data retrieved by the search function 42 and creates charts such as pie charts and bar graphs, or calculates the total needle usage time of equipment and parts necessary for reliability analysis. It performs calculations such as failure frequency calculation and analysis of failure occurrence over time, and outputs and displays them from the output device 6.

For example, the total needle time calculation for equipment/parts is performed as follows.

(a) Calculation of total test time at component level Apply total test time calculation in fixed-time truncated life test method for products that cannot be repaired T = 2: t4 + (n - γ) t c -''・■
iMaro 1 Where, to: Test discontinuation time (replacement cycle of normal replacement parts) tl: Service life time until occurrence of each failure γ: Number of failures n: Number of samples (total parts) (b) Equipment level Calculation of total needle time Applying the calculation of total test time in fixed time cut-off life test method for repairable products T = ntc...■Here, t
c: Test termination time (service life of each device) n: Number of samples (equipment) However, depending on the model, the time excluding repair and inspection time is applied.

Calculation of the frequency of failure occurrence is performed, for example, as follows.

Tsuneguchi: R Used for evaluation 2 analysis of the frequency of occurrence of abnormal parts during normal (regular) replacement and signs of abnormality against the total number of inspected parts.

R8=-X100・
...■i Here, nB: Total number of abnormal parts during normal (regular) replacement nl: Total number of inspected parts: R Relative to the total number of replaced parts. While driving or normal (regular)
Used for evaluation 2 analysis of the total number of parts replaced outside of the replacement period and problems that have become apparent.

R, = −X 100...)
n Nicode, nF: Total number of replacement parts during operation or outside the normal (regular) replacement cycle nc: Total number of replacement parts Trouble: R Frequency of trouble occurrence relative to the total number of equipment inspections.

Used for R to evaluate device reliability = -x too...■
In C22, n = number of trouble occurrences nc: total number of equipment inspections Next, the reliability analysis function 44 calculates the reliability and lifespan of equipment and parts, outputs and displays them from the output device 6, and displays them in the field. Provide operators with the reliability and lifespan of a piece of equipment based on data.

The reliability and lifespan of equipment and parts are provided as MTBF, failure rate λ, failure mode, and failure distribution form. These calculation methods are as follows.

Assuming that failure occurrence follows an exponential distribution (even failure region), the failure rate of equipment/components, MTB
It becomes possible to estimate F.

The failure rate and MTBF are used as a measure of the reliability of equipment and parts, and for comparison and evaluation with maintenance cycles.

Estimation of constant average failure rate and MTBF I T , tt MTBF=-=-=□...■ λ γ γ Where, T: Total service life γ: Number of failures MTBF: Point Estimated MTBF λ: Point estimated failure rate n: Number of samples: Number of service life for each sample - ku! Estimation Estimation of variance value failure rate, MTBF when a certain statistical confidence level is set.Here, (M T B F )L: Lower confidence limit M
TBF (MTBF) υ: Upper confidence limit of MT BF λV: Lower confidence limit of failure rate λU: Upper confidence limit of failure γ: Number of failures α: Risk rate (1-α): Confidence level χ” (a t b) : Value of the 12-distribution with degree of freedom a and upper probability S: T : In a small interval estimation with a total needle time of 0, even if the number of failures is 0, the failure rate and MTBF can be calculated by assuming a certain confidence level and an exponential distribution of failures. It can be estimated.

This can be evaluated as a measure of the reliability of the device/component relative to the amount of total needle use time, that is, the usage history.

The failure distribution form is estimated by applying Weibull analysis or Weibull type cumulative hazard analysis.

The Weibull distribution function F(t) is expressed by the following equation, and its probability density function f (t) is expressed by the following equation.

(t≧Or to>O, m>O, t≧γ) where 1m
: Shape parameter to: Scale parameter γ : Position parameter t : Time shape parameter m represents the form of failure according to the following classification, and is used for evaluation of failure status and maintenance measures.

m < 1 Initial failure (failure rate decreasing type) m = 1 Random failure (failure rate constant type) m > 1 Wear-out failure (failure rate increasing type) Also, the scale parameter t0 represents the magnitude of the life.

Other first-order parameters can also be applied to determine the maintenance status.

(a) Characteristic life: η(E(t)) When t=η, the survival rate is 36.8%, which corresponds to the average life of an exponential distribution.E(t) Cape 1. =η...(11)
(b) Average life 2μ (c) Variance: σ2 (d) Reliability life: ργ Value of t such that reliability function R(t) is equal to γ - r town) + 1) ・°・ γ = e' ″ (°, ° γ
=O)-r”<L+x+ (The average life μ is the reliable life with reliability e 1.)
The cumulative failure distribution function F (t) is calculated using the following formula.

(a) General method F (tt) = i /n
= (15) (b) Average rank method F(tt) = (i −0
,5)/n-(16)(c) Mode rank method
F(tt)=(i-1)/(n-1)-(17)(
d) Median rank method F(t,)=H'″”(0,5
1i, n i+1) t( (Johnson method) Applicability of rank method 1+n-Σy Where, yt: Number of failures at 1 point n: Number of samples tγL: Cumulative number of failures up to 1 point (f) Cumulative hazard method Instantaneous Method of directly using cumulative failure rate Normally, the above (a) to (d) are applied to "complete data".However, if the number of data is small (generally n<2
0), it is said that (b) to (d) are desirable.

On the other hand, for "incomplete data", (e), (f)
apply.

The actual estimation of the Weibull parameter is performed by fitting a straight line to the two-parameter Weibull distribution by -order regression calculation, as shown in the following equation.

That is, a=1/m, Y=nnffn(1-F(t)
)-”b=ant, X=12nt.

X0a Y= b, and (X, Y) = (Qnt, QnQn(1-F(t
))'1) By linear regression using the formula X+aY=b and determining a and b, the shape parameter m2 scale parameter t is determined. Estimate.

As a modification of Weibull analysis, a method is also applied in which a cumulative hazard function is estimated by accumulating instantaneous failure rates, and the failure distribution is evaluated by Weibull type cumulative hazard analysis.

Finally, the maintenance status diagnosis function 45 of the present invention will be explained.

FIG. 3 shows a series of data processing/processing flows (equipment maintenance status diagnosis method) until maintenance guidance is provided by each function of search, statistical processing, reliability analysis, and maintenance status diagnosis.

First, a device to be inspected for maintenance status is selected according to an operator's request/instruction, and when the input is entered, the maintenance response for that device is selected. By searching the device maintenance standard information in the storage device, if the device maintenance cycle data is determined, it is certified as a preventive maintenance device, and if the maintenance cycle data is not fixed, it is certified as a corrective maintenance device. Ru.

Device maintenance status diagnosis is performed using different methods for devices that require preventive maintenance and devices that require corrective maintenance.

In the former case, the validity of the current equipment inspection cycle and parts replacement cycle is the subject of diagnosis; in the latter case, the need for overhaul and replacement (appropriate This is because the subject of diagnosis is whether the maintenance period has come.

Below, we will take a pump for the reactor coolant purification system of a boiling water nuclear power plant (hereinafter referred to as a CUW pump; equipment for preventive maintenance) and a dot recorder (equipment for corrective maintenance) as examples.
The equipment maintenance management support device and maintenance status diagnosis method according to the present invention will be explained along the flow of the diagram.

(Case 1) In the case of equipment compatible with preventive maintenance In the case of equipment compatible with preventive maintenance, failures and failures stored in the storage device
Malfunction history and maintenance history information are searched, and data to be diagnosed regarding maintenance status is extracted.6 For example, as shown in Figure 4, in the case of a CUW pump, there are 3 plants, 12 operating cycles, 12 regular inspections, A table showing that information for 24 equipment inspection experiences is currently registered is displayed from the output device.

Next, information regarding the equipment specifications, environment, and usage conditions of the CUW pump included in this diagnostic data is searched.
CUW pump diagnostic target data with similar conditions are selected, and the analysis target population is tested.

Next, the component parts information of the equipment and the maintenance standard information of the equipment/parts are searched, and the parts to be diagnosed are expanded and the maintenance (replacement/inspection) performance of each part, maintenance (replacement cycle) standards, quantity, etc. Part unit analysis base data is extracted. For example, in the case of a CUW pump, a table as shown in FIG. 5 is output and displayed from the output device.

Next, based on this part-by-part analysis data, we calculate the occurrence rate of abnormal parts by calculating the number of failures (1 category, 1 equation, 2 equations).
Statistical processing such as unscheduled replacement rate is performed, and furthermore, based on the cause of the failure that has occurred or experienced, it is determined whether the failure is a deterioration failure, an accidental failure, or
The initial failure is classified into which failure mode it is classified into by comparing it with failure mode classification information that has been classified and organized in a storage device in advance. Then, according to the failure mode classification, component failure analysis is performed, such as calculating the failure rate for each classification.For example, in the example of a CUW pump, a table showing the analysis results as shown in Figure 6 is output. The information is displayed and displayed to the operator by the device.

Next, based on the above-mentioned part-by-part analysis data.

Calculate total needle time by formula, MTB by 2〇,■,■
In addition to F estimation, a comparison of the maintenance standard information and MTBF (for example, the difference between the two) is performed, or if the number of data is more than a certain number, component reliability analysis such as Weibull analysis using 20 to (2o) is performed. For example, a table of the results of one part reliability analysis as shown in FIG. 7 is output and displayed to the operator from the output device, and serves as a support for maintenance work.

Next, the maintenance status of each part is diagnosed based on the evaluation criteria based on the expert's knowledge and experience of the parts failure analysis results, the nine parts reliability analysis results, and the results.

In the equipment maintenance status diagnosis method, the diagnosis results of the maintenance status are ranked on a scale of extension-adequacy-shortening (setting of final goals). This sets the scale of extension, adequacy, and shortening to 5 levels for the current status of the replacement cycle of 0, for example, 1 part, which corresponds to the rank of the middle determination result in FIG. ■Evaluation elements related to this final goal, in this case, information (evaluation elements) regarding the reliability and lifespan of equipment and parts obtained from arithmetic processing functions are classified in a hierarchical manner. This classification is based on experts' maintenance status judgment procedures, such as first failure frequency 2.
After evaluating the occurrence rate, we then analyze the details of the failure (failure mode), and based on this result, we can determine the failure frequency 2.
Corrected the evaluation based on incidence.

Furthermore, classification and hierarchization are performed according to procedures such as determining the validity of the replacement cycle of a part by taking into account the degree of failure influence and maintenance difficulty of each part. ) This can be done as shown in the maintenance judgment flow.

,Next, ■ At each hierarchical evaluation stage, the rank of each evaluation element is determined according to the expert's maintenance standards given as maintenance standard information. ■ The ranking of highly relevant evaluation elements is determined by applying a matrix. Enables consideration of conservation status evaluation at the same time. As shown in Figure 8 (■~), this requires two highly related evaluations! For example, according to this method, the unscheduled replacement rate (actualized failure) and abnormal parts occurrence rate (failure This makes it possible to rank items that take into account symptoms (symptoms) at the same time.

There are many evaluation criteria, for example, in the case of failure rate.

There are relatively many cases, but there are a few cases. The failure rate is ranked by determining the failure rate using a linear equation.

(Many) ← (Failure occurrence rate)〉xl (Relatively common) ←

(No) ← (Failure occurrence rate) = 0 Here, for X-engine # Alternatively, when performing diagnosis, the Kugata device is inputted, modified, and used.

Finally, ■The rank judgment results for each evaluation element obtained at each hierarchical stage are weighted as necessary, and the rank of the diagnosis result set in (■) above (the rank set as the final goal) is estimated. This corresponds to FIG. 8 0) and is output and displayed as part maintenance guidance from the output device.

Next, the maintenance status of the equipment is diagnosed based on the results of this parts maintenance guidance. This is shown in Figure 8 from 0 to (
As shown in c), if there is a cycle shortening guide for parts (consumables) that are replaced every equipment inspection among the parts subject to 5 diagnosis, it is determined that it is necessary to shorten the equipment inspection cycle. In addition, if all of the consumable parts are estimated to have extended or conditional extension guides, and there are no shortened guides that affect the equipment inspection cycle for parts whose parts replacement cycle is longer than the equipment inspection cycle, TF & equipment inspection will be carried out. A period extension possibility guide is determined.

Furthermore, when the possibility of extending the equipment inspection cycle is finally diagnosed, maintenance information such as equipment failure impact information is displayed and output from the output device.For example, in the case of a CUW pump, the Maintenance guidance as shown is provided to the operator as decision support information.

(Case 2) In the case of equipment that supports corrective maintenance In the case of equipment that supports corrective maintenance, failures and
Malfunction information is searched and data to be diagnosed in a particular plant is extracted. Also, similar to (Case 1), the analysis target population is tested.

Next, from this diagnosis target data, equipment analysis target data is searched for each failure mode (failure part - cause combination).
The total is classified into 2. For example, in the case of a dot type recorder, classification and aggregation are performed as shown in FIG. 10, and analysis base data for each failure mode is extracted and displayed/outputted from an output device.

Next, reliability analysis such as calculating the total number of years of needle use using formula ②, estimating MTBF using 20~(8), and Weibull analysis using 20)~(2o) is performed. In the example, reliability analysis results as shown in FIG. 11 are output and displayed from the output device.

Next, based on these reliability analysis results, overhaul is performed according to the flow shown in FIG.

The need for replacement is diagnosed. In other words, the maintenance status of the equipment can be diagnosed based on whether the cause of the failure is classified as deterioration, accidental, or early failure, the MTBF (failure rate) value, and the failure distribution form estimated from the Weibull coefficient. be done. This result is output and displayed from the blade device as a maintenance guide as shown in FIG. 13, for example.

Furthermore, the results of the reliability analysis, such as the Weibull analysis results shown in FIG. 14, are also output.

As described above, it is possible to support the decision-making of the operator (repair personnel) regarding maintenance work and management using a series of output information or maintenance guidance information as shown in FIGS. 9 and 13.

〔Effect of the invention〕

As explained above, according to the present invention, the life span and
Failure due to prediction, evaluation and management of deterioration.

It reduces or prevents the occurrence of defects, improves the reliability of large plants such as nuclear power plants, and their equipment, and enables appropriate maintenance, improving plant operating rates and reducing maintenance costs.

Economic efficiency can be improved by reducing equipment costs. Furthermore, since the reliability of the conventional evaluation of life deterioration of equipment parts can be improved, the excellent effect of reducing the labor and time required for managing plant equipment and parts can be achieved.

[Brief explanation of the drawing]

FIG. 1 is a block diagram of an embodiment of the present invention. FIG. 2 is a block diagram for explaining the functions and information processing flow of the arithmetic processing device in FIG. 1, and FIG. 3 is a diagram showing the processing flow for equipment maintenance status diagnosis in the arithmetic processing device in FIG. Figure 4 shows the data registration history display of the data to be analyzed, Figure 5 shows the component unit analysis base data, Figure 6 shows the results of component defect analysis, and Figure 7 shows the results of component reliability analysis. Figure 8 is a diagram showing the maintenance judgment method.
Figure 9 is a diagram showing maintenance guidance for equipment subject to preventive maintenance;
Figure 10 is a diagram showing the results of equipment failure analysis, Figure 11 is a diagram showing the results of equipment reliability analysis, Figure 12 is a flow diagram for explaining maintenance guidance for equipment that supports corrective maintenance, and Figure 13 is a diagram showing the results of equipment failure analysis.
The figure shows maintenance guidance for equipment subject to corrective maintenance.
FIG. 4 is a diagram showing the Weibull analysis results. 1...Input information 2...Operator request/command 3
...Input bag [4...Arithmetic processing device 5...Storage device 6...Output device Agent Patent attorney Yoshiaki Inomata (
(and 1 other person) Figure 1 Figure 3 Figure 4 Country Figure 6 Figure 9 Figure ■ (Kencho Uta Anabe Rii Bazen Gaidas 3) Figure 12 Figure 1

Claims (3)

[Claims] (1) an input device for inputting information regarding equipment maintenance and evaluation criteria for equipment maintenance status determination information, as well as operator requests/instructions, and a storage device for storing information input from the input device; A function that performs input/output processing, search, statistical processing, and reliability analysis of information stored in the storage device according to operator requests and instructions input from an input device, and reliability of equipment parts obtained by the function.・It is composed of a processing unit that has an equipment maintenance status diagnosis function that ranks equipment maintenance status based on information regarding lifespan, and an output device that outputs and displays information obtained from the processing unit to the operator. A featured equipment maintenance management support device. (2) The device according to claim 1, wherein the information regarding the maintenance of the device is device component information, failure/malfunction history information, device operation/use history information, maintenance history information, and device design specification/maintenance standard information. Maintenance management support equipment. (3) The equipment maintenance management support device according to claim 1, wherein the information obtained from the arithmetic processing unit is information necessary for equipment maintenance management.