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Design considerations of computer-aided RCM-based plant maintenance management system
European Symposium on Computer Aided Process Engineering - 11 R. Gani and S.B. Jorgensen (Editors) 9 2001 Elsevier Science B.V. All rights reserved.
859
Design considerations of computer-aided RCM-based plant maintenance management system Hossarn A.Gabbar, Kazuhiko Suzuki, and Yukiyasu Shimada Department of Systems Engineering, Okayama University 3-1-1 Tsushima-Naka, 700-8530 Okayama City, Okayama, Japan In most of the industries, Reliability-Centered Maintenance (RCM) process hasn't been fully integrated with the computerized maintenance management system (CMMS) where they run in isolation leading to outdated maintenance strategies. In this paper, the idea of integrating RCM process with the CMMS has been studied so that the maintenance strategies are dynamically changing throughout the plant lifecycle. The integrated solution has been discussed in two-folds: (1) Information, where plant design model is obtained from the plant design environment, while plant operational information is acquired from the operational systems; (2) Process logic where HAZOP, FMECA, and FTA are utilized to analyze and assess all failure types in quantitative manner, while combined Monte Carlo, Genetic algorithm, and Weibull probability distribution functions (pdf) are employed to optimize the maintenance tasks. The activity and functional models of the enhanced RCM process are developed. Modifications to the different modules of CMMS (i.e. MAXIMO TM)are proposed. 1. INTRODUCTION RCM process is intended to determine the most realistic and optimized maintenance requirements of any physical asset to continue its stated operating condition [1]. Many industries are adopting RCM technique to solve many confronted maintenance problems. Unfortunately, it didn't work as expected for many reasons: (1) RCM is a time consuming process and requires considerable amount of resources, especially for large number of assets; (2) information is not adequate to decide the suitable maintenance strategy and to optimize its cost; (3) there are non-engineering factors involved in the maintenance problems i.e. management. To achieve the expected results, it is essential to consider an integrated solution using the plant lifecycle's accumulated information. By considering quantitative asset and failure assessment techniques accurate results can be achieved. Combined HAZOP, FTA, and FMECA are used to analyze the root cause of each failure. Combined Monte Carlo simulation,
860 Genetic algorithm, and Weibull pdf are used to optimize maintenance tasks. Previously, attempts are made to integrate RCM within the design environment using fuzzy reasoning algorithms [2]. This research proposes wider vision of integration that includes plant design and operational systems, and suggests the necessary design changes to CMMS. 2. PROPOSED SYSTEM ARCHITECTURE
Fig. 1 - RCM-based integrated system architecture. Fig. 1 shows the system flowchart of the proposed integrated solution, which includes: plant design environment, operational systems, CMMS, and RCM process. The cycle starts from the plant design by developing the plant model, which includes the plant static (structure and topology), behavior, and function views. Plant asset information is extracted from the plant static model into CMMS and passed to RCM process. Failure model is developed within the plant design using HAZOP, FTA, and FMECA techniques. The failure model in the plant design domain is translated into failure hierarchies within CMMS, which is passed to RCM process for failure assessment. Assets are represented within CMMS as templates (or class definitions) and physical asset records. For example, Pumps and Valves are represented as class definitions, while the physical plant assets for pumps and valves are represented as asset records, which are associated with the corresponding asset templates. Asset information is passed to the RCM process for asset assessment practice. Maintenance strategies are initially decided during the design stage and gradually tuned throughout the plant lifecycle using feedback information from operational systems and reliability data. The data flow "D8 ") P2" is used to express the information flow from D8 (the maintenance history data within CMMS) to P2 (RCM), which
861 fortifies the utilization of the reliability data from the running CMMS. 3. RCM PROCESS MODELING
Object-oriented modeling approach has been adopted to analyze and enhance RCM process. The activity and functional models have been developed for this purpose, as below. 3.1.
RCM Activity Model
Fig. 2 - RCM process first-level activity model. The activity model has been developed using IDEFO where the RCM process has been enhanced from both business and technical views (from "As-Is" to "To-Be"). IDEFO is useful in establishing the scope of the analysis, especially for functional analysis. Fig. 2 shows the first-level activity model, which shows four major activities: (1) manage the RCM process where scope and resources are identified, (2) identify assets, functions, and performance standards where plant structure information is mapped from the plant design model, (3) analyze failure, cause, consequences, and evaluate associated risk, and (4) decide and optimize maintenance strategies and tasks. The integration on the activity model level is applied to augment the corresponding activities from CMMS and to understand the required input / output information and its originality i.e. plant design or operational systems. For example, asset identification activity has been consolidated to read the plant model from plant design environment. It creates the asset templates as the base classes for the associated assets.
862
Fig. 3 - RCM Process Function Decomposition. 3.2.
RCM Functional Modeling
Using the activity model, the function decomposition of the proposed RCM process can be constructed (as in Fig. 3). RCM can be decomposed into the following functions: assess assets, assess failures, decide maintenance strategy, decide maintenance tasks, optimize maintenance tasks, and check & validate the results. Maintenance tasks are defined as: lubrication, cleaning, inspection, replacement, repair, fabrication, and overhaul, which are used within the expert system (decision engine) to decide the suitable maintenance task based on the failure, consequence,
reliability data, accumulated
maintenance
history, and design model
requirements (including safety requirements). Maintenance task level is classified as: good-as-new (GAN), imperfect, and bad-as-old (BAO). Soft and hard life factors [3] are optimized using combined Monte Carlo simulation and Genetic algorithm (explained in details in reference [4]). Weibull function is used to model failure behavior and classify failures, which is used to construct the reliability data. Checker report using RCM knowledgebase and maintenance data is used to verify the correctness and accuracy of the obtained results (i.e. optimized maintenance tasks).
863 4. INTEGRATED SYSTEM DESIGN
Fig. 4 shows the detailed design of the integrated solution where plant model is represented in three dimensions: structure (static information about the plant i.e. Pump-1 and Valve-I), dynamic (behavioral information about the fluid, process variables changes with time i.e. fluid-pressure-out-Pump-l, which is represented by set of equations), and functional (operations performed by plant objects i.e. Valve-1 --) Open). HAZOP is used to report the possible deviations, causes, and consequences for Pump-l, and FTA reflects all the possible paths for each top-failure with the probability of failure for each minimal cut set, while FMECA defines the different failures with the different levels of details along with the criticality of each failure. Combination of these failure assessment techniques facilitates the analysis of the root cause. Assessing the root cause instead of the actual cause reduces the probability of failure and hence minimizes the associated risks.
~l
A
PlantOO ,'" u// /
, ',]
D~ Functional I
r ~ ....... r...... } I c.... q..... l / . ~ FallureModel~ ~
I
J
CMMS ~
AssetData (
I ' " , - . , ~ / ~ / ~ 7 '~'~-/~ / (Fa,lureData ( /
l
\/ / X/
.............
Fig. 4 - System flowchart of the proposed integrated solution. 5. CMMS DESIGN MODIFICATIONS RCM engine is developed using C++ as a module within MAXIMO TM (as in Fig. 5). The database structure of the failure module is modified to include failure-asset relationship and to maintain the link with the reliability data. Failure date is maintained using combined FTA/HAZOP/FMECA. RCM engine is invoked to validate changed or newly created maintenance tasks (or for new equipment), and to decide and optimize maintenance strategies. An intelligent translator is used to translate the plant design information into Asset module.
864
Fig. 5 - CMMS Design Modifications 6. CONCLUSION RCM-based CMMS is used to optimize maintenance for critical plants. The developed activity and function models are useful to understand such solution and to analyze other plants with minor modifications saving analysis time. The design modifications proposed to the adopted CMMS can be realized within MAXIMO while RCM engine can be developed as a shell integrated with the different modules of MAXIMO. The integration with the plant design and operational systems is essential to share and utilize plant design model and plant operational information. Combined HAZOP, FMECA, and FTA are used to assess failure comprehensively and quantitatively. REFERENCES
1. John Moubray. Reliability-centered maintenance, Butterworth Heinemann, ISBN 0 7506 3358 1 (1997). 2. D.J. Fonseca and G.M. Knapp. Expert Systems with Applications, No. 19 (2000), 45-57. 3. J. Crocher and U.D. Kumar. Reliability Engineering & System Safety, No. 67 (2000), 113-118. 4. M. Marseguerra and E. Zio. Reliability Engineering & System Safety, No. 68 (2000), 69-83.
859
Design considerations of computer-aided RCM-based plant maintenance management system Hossarn A.Gabbar, Kazuhiko Suzuki, and Yukiyasu Shimada Department of Systems Engineering, Okayama University 3-1-1 Tsushima-Naka, 700-8530 Okayama City, Okayama, Japan In most of the industries, Reliability-Centered Maintenance (RCM) process hasn't been fully integrated with the computerized maintenance management system (CMMS) where they run in isolation leading to outdated maintenance strategies. In this paper, the idea of integrating RCM process with the CMMS has been studied so that the maintenance strategies are dynamically changing throughout the plant lifecycle. The integrated solution has been discussed in two-folds: (1) Information, where plant design model is obtained from the plant design environment, while plant operational information is acquired from the operational systems; (2) Process logic where HAZOP, FMECA, and FTA are utilized to analyze and assess all failure types in quantitative manner, while combined Monte Carlo, Genetic algorithm, and Weibull probability distribution functions (pdf) are employed to optimize the maintenance tasks. The activity and functional models of the enhanced RCM process are developed. Modifications to the different modules of CMMS (i.e. MAXIMO TM)are proposed. 1. INTRODUCTION RCM process is intended to determine the most realistic and optimized maintenance requirements of any physical asset to continue its stated operating condition [1]. Many industries are adopting RCM technique to solve many confronted maintenance problems. Unfortunately, it didn't work as expected for many reasons: (1) RCM is a time consuming process and requires considerable amount of resources, especially for large number of assets; (2) information is not adequate to decide the suitable maintenance strategy and to optimize its cost; (3) there are non-engineering factors involved in the maintenance problems i.e. management. To achieve the expected results, it is essential to consider an integrated solution using the plant lifecycle's accumulated information. By considering quantitative asset and failure assessment techniques accurate results can be achieved. Combined HAZOP, FTA, and FMECA are used to analyze the root cause of each failure. Combined Monte Carlo simulation,
860 Genetic algorithm, and Weibull pdf are used to optimize maintenance tasks. Previously, attempts are made to integrate RCM within the design environment using fuzzy reasoning algorithms [2]. This research proposes wider vision of integration that includes plant design and operational systems, and suggests the necessary design changes to CMMS. 2. PROPOSED SYSTEM ARCHITECTURE
Fig. 1 - RCM-based integrated system architecture. Fig. 1 shows the system flowchart of the proposed integrated solution, which includes: plant design environment, operational systems, CMMS, and RCM process. The cycle starts from the plant design by developing the plant model, which includes the plant static (structure and topology), behavior, and function views. Plant asset information is extracted from the plant static model into CMMS and passed to RCM process. Failure model is developed within the plant design using HAZOP, FTA, and FMECA techniques. The failure model in the plant design domain is translated into failure hierarchies within CMMS, which is passed to RCM process for failure assessment. Assets are represented within CMMS as templates (or class definitions) and physical asset records. For example, Pumps and Valves are represented as class definitions, while the physical plant assets for pumps and valves are represented as asset records, which are associated with the corresponding asset templates. Asset information is passed to the RCM process for asset assessment practice. Maintenance strategies are initially decided during the design stage and gradually tuned throughout the plant lifecycle using feedback information from operational systems and reliability data. The data flow "D8 ") P2" is used to express the information flow from D8 (the maintenance history data within CMMS) to P2 (RCM), which
861 fortifies the utilization of the reliability data from the running CMMS. 3. RCM PROCESS MODELING
Object-oriented modeling approach has been adopted to analyze and enhance RCM process. The activity and functional models have been developed for this purpose, as below. 3.1.
RCM Activity Model
Fig. 2 - RCM process first-level activity model. The activity model has been developed using IDEFO where the RCM process has been enhanced from both business and technical views (from "As-Is" to "To-Be"). IDEFO is useful in establishing the scope of the analysis, especially for functional analysis. Fig. 2 shows the first-level activity model, which shows four major activities: (1) manage the RCM process where scope and resources are identified, (2) identify assets, functions, and performance standards where plant structure information is mapped from the plant design model, (3) analyze failure, cause, consequences, and evaluate associated risk, and (4) decide and optimize maintenance strategies and tasks. The integration on the activity model level is applied to augment the corresponding activities from CMMS and to understand the required input / output information and its originality i.e. plant design or operational systems. For example, asset identification activity has been consolidated to read the plant model from plant design environment. It creates the asset templates as the base classes for the associated assets.
862
Fig. 3 - RCM Process Function Decomposition. 3.2.
RCM Functional Modeling
Using the activity model, the function decomposition of the proposed RCM process can be constructed (as in Fig. 3). RCM can be decomposed into the following functions: assess assets, assess failures, decide maintenance strategy, decide maintenance tasks, optimize maintenance tasks, and check & validate the results. Maintenance tasks are defined as: lubrication, cleaning, inspection, replacement, repair, fabrication, and overhaul, which are used within the expert system (decision engine) to decide the suitable maintenance task based on the failure, consequence,
reliability data, accumulated
maintenance
history, and design model
requirements (including safety requirements). Maintenance task level is classified as: good-as-new (GAN), imperfect, and bad-as-old (BAO). Soft and hard life factors [3] are optimized using combined Monte Carlo simulation and Genetic algorithm (explained in details in reference [4]). Weibull function is used to model failure behavior and classify failures, which is used to construct the reliability data. Checker report using RCM knowledgebase and maintenance data is used to verify the correctness and accuracy of the obtained results (i.e. optimized maintenance tasks).
863 4. INTEGRATED SYSTEM DESIGN
Fig. 4 shows the detailed design of the integrated solution where plant model is represented in three dimensions: structure (static information about the plant i.e. Pump-1 and Valve-I), dynamic (behavioral information about the fluid, process variables changes with time i.e. fluid-pressure-out-Pump-l, which is represented by set of equations), and functional (operations performed by plant objects i.e. Valve-1 --) Open). HAZOP is used to report the possible deviations, causes, and consequences for Pump-l, and FTA reflects all the possible paths for each top-failure with the probability of failure for each minimal cut set, while FMECA defines the different failures with the different levels of details along with the criticality of each failure. Combination of these failure assessment techniques facilitates the analysis of the root cause. Assessing the root cause instead of the actual cause reduces the probability of failure and hence minimizes the associated risks.
~l
A
PlantOO ,'" u// /
, ',]
D~ Functional I
r ~ ....... r...... } I c.... q..... l / . ~ FallureModel~ ~
I
J
CMMS ~
AssetData (
I ' " , - . , ~ / ~ / ~ 7 '~'~-/~ / (Fa,lureData ( /
l
\/ / X/
.............
Fig. 4 - System flowchart of the proposed integrated solution. 5. CMMS DESIGN MODIFICATIONS RCM engine is developed using C++ as a module within MAXIMO TM (as in Fig. 5). The database structure of the failure module is modified to include failure-asset relationship and to maintain the link with the reliability data. Failure date is maintained using combined FTA/HAZOP/FMECA. RCM engine is invoked to validate changed or newly created maintenance tasks (or for new equipment), and to decide and optimize maintenance strategies. An intelligent translator is used to translate the plant design information into Asset module.
864
Fig. 5 - CMMS Design Modifications 6. CONCLUSION RCM-based CMMS is used to optimize maintenance for critical plants. The developed activity and function models are useful to understand such solution and to analyze other plants with minor modifications saving analysis time. The design modifications proposed to the adopted CMMS can be realized within MAXIMO while RCM engine can be developed as a shell integrated with the different modules of MAXIMO. The integration with the plant design and operational systems is essential to share and utilize plant design model and plant operational information. Combined HAZOP, FMECA, and FTA are used to assess failure comprehensively and quantitatively. REFERENCES
1. John Moubray. Reliability-centered maintenance, Butterworth Heinemann, ISBN 0 7506 3358 1 (1997). 2. D.J. Fonseca and G.M. Knapp. Expert Systems with Applications, No. 19 (2000), 45-57. 3. J. Crocher and U.D. Kumar. Reliability Engineering & System Safety, No. 67 (2000), 113-118. 4. M. Marseguerra and E. Zio. Reliability Engineering & System Safety, No. 68 (2000), 69-83.