Pub Date : 2022-09-21DOI: 10.1108/jqme-03-2022-0019
Imane Mjimer, Es-Saâdia Aoula, E. H. Achouyab
PurposeThe aim of this study is to predict one of the key performance indicators used to improve continually production systems using machine learning techniques known by the ability to teach the machine to perform complex things as opposed to simple statistical methods by giving this machine the historical dataset, according to the kind of machine learning the authors will use, the machine will be able to predict a new output data from the input data given by the user.Design/methodology/approachThis work is divided into six sections: In the first section, the state of art for OEE, machine learning, and regression models. In the second section, the methodology, followed by an experimental study conducted in an automotive company specialised in the manufacturing of manual transmissions.FindingsThe three models show a very high accuracy (higher than 99%), a comparison between these three models was done using three indicators, namely mean absolute error (MAE) mean square error (mean squared error and mean absolute percentage error which shows that the best model is the least angle followed by Bayesian Ridge and automatic relevance determination regression.Originality/valueAs the authors can see many works were done in the different production systems for prediction, the most relevant works were done to predict a parameter in the production system such as The prediction of part thickness in aluminium hot stamping process with partition temperature control the prediction of CO2 trapping performance the prediction of crop yield the prediction of lean manufacturing in automotive parts industry the contribution of the work will be to use the machine learning techniques to predict the key performance indicator “used to measure manufacturing efficiency” which is the overall equipment effectiveness used in the authors’ case to measure the improvement of the production system.
{"title":"Contribution of machine learning in continuous improvement processes","authors":"Imane Mjimer, Es-Saâdia Aoula, E. H. Achouyab","doi":"10.1108/jqme-03-2022-0019","DOIUrl":"https://doi.org/10.1108/jqme-03-2022-0019","url":null,"abstract":"PurposeThe aim of this study is to predict one of the key performance indicators used to improve continually production systems using machine learning techniques known by the ability to teach the machine to perform complex things as opposed to simple statistical methods by giving this machine the historical dataset, according to the kind of machine learning the authors will use, the machine will be able to predict a new output data from the input data given by the user.Design/methodology/approachThis work is divided into six sections: In the first section, the state of art for OEE, machine learning, and regression models. In the second section, the methodology, followed by an experimental study conducted in an automotive company specialised in the manufacturing of manual transmissions.FindingsThe three models show a very high accuracy (higher than 99%), a comparison between these three models was done using three indicators, namely mean absolute error (MAE) mean square error (mean squared error and mean absolute percentage error which shows that the best model is the least angle followed by Bayesian Ridge and automatic relevance determination regression.Originality/valueAs the authors can see many works were done in the different production systems for prediction, the most relevant works were done to predict a parameter in the production system such as The prediction of part thickness in aluminium hot stamping process with partition temperature control the prediction of CO2 trapping performance the prediction of crop yield the prediction of lean manufacturing in automotive parts industry the contribution of the work will be to use the machine learning techniques to predict the key performance indicator “used to measure manufacturing efficiency” which is the overall equipment effectiveness used in the authors’ case to measure the improvement of the production system.","PeriodicalId":16938,"journal":{"name":"Journal of Quality in Maintenance Engineering","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44860399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-06DOI: 10.1108/jqme-04-2021-0032
Mohammad AliFarsi
PurposeUnmanned aircraft applications are quickly expanded in different fields. These systems are complex that include several subsystems with different types of technologies. Maintenance and inspection planning is necessary to obtain optimal performance and effectiveness. The failure rate in these systems is more than commercial and manned aircraft since they are usually cheaper. But maintenance and operation planning are difficult because we deal with a system that has multi-components, multi-failure models, and different dependencies between subsystems without any advanced health monitoring system. In this paper, this matter is considered and a framework to determine optimal maintenance and inspection plan for this type of system is proposed to improve system reliability and availability. The new criteria according to this field are proposed.Design/methodology/approachMaintenance of unmanned systems influences their readiness; also, according to the complexity of the system and different types of components, maintenance programming is a vital requirement. The plan should consider several criteria and disciplines; thus, multicriteria decision approaches may be useful. On another side, the reliability and safety of unmanned aircraft are the most important requirements in the design and operation phases. The authors consider these parameters and develop a framework based on risk-based maintenance to overcome the problems for unmanned systems. This framework consists of two stages: at the first stage, the critical components and failure modes are determined by FMEA, and in the second stage, the priority of maintenance tasks is determined by a fuzzy multicriteria weighted decision system. In this study, fourteen criteria with different levels of importance are developed and proposed to find the best plan for maintenance and inspection intervals. These criteria have been extracted from the literature review, the author's experience, and expert opinions.FindingsA novel framework for risk-based maintenance has been proposed. Risk determination and risk criteria are the most important factors in this framework. Risks are determined by FMEA, and new criteria are proposed that are used for decision-making. These criteria are proposed based on practical experience and experts' opinions for the maintenance process in the aeronautic industry. These are evaluated by industrial cases, and this framework capability has been demonstrated.Research limitations/implicationsThe proposed framework and criteria for small unmanned aircraft have been developed based on a practical point of view and expert opinion. Thus for implementation in other aeronautic industries, the framework may need a minor modification.Practical implicationsTwo important subsystems of an unmanned aircraft have been studied, and the capabilities of this method have been presented.Originality/valueThis research is original work to determine a maintenance program for unmanned aircraft that their
{"title":"RBM-MCDM framework for optimization of maintenance and inspection intervals of small unmanned aircrafts","authors":"Mohammad AliFarsi","doi":"10.1108/jqme-04-2021-0032","DOIUrl":"https://doi.org/10.1108/jqme-04-2021-0032","url":null,"abstract":"PurposeUnmanned aircraft applications are quickly expanded in different fields. These systems are complex that include several subsystems with different types of technologies. Maintenance and inspection planning is necessary to obtain optimal performance and effectiveness. The failure rate in these systems is more than commercial and manned aircraft since they are usually cheaper. But maintenance and operation planning are difficult because we deal with a system that has multi-components, multi-failure models, and different dependencies between subsystems without any advanced health monitoring system. In this paper, this matter is considered and a framework to determine optimal maintenance and inspection plan for this type of system is proposed to improve system reliability and availability. The new criteria according to this field are proposed.Design/methodology/approachMaintenance of unmanned systems influences their readiness; also, according to the complexity of the system and different types of components, maintenance programming is a vital requirement. The plan should consider several criteria and disciplines; thus, multicriteria decision approaches may be useful. On another side, the reliability and safety of unmanned aircraft are the most important requirements in the design and operation phases. The authors consider these parameters and develop a framework based on risk-based maintenance to overcome the problems for unmanned systems. This framework consists of two stages: at the first stage, the critical components and failure modes are determined by FMEA, and in the second stage, the priority of maintenance tasks is determined by a fuzzy multicriteria weighted decision system. In this study, fourteen criteria with different levels of importance are developed and proposed to find the best plan for maintenance and inspection intervals. These criteria have been extracted from the literature review, the author's experience, and expert opinions.FindingsA novel framework for risk-based maintenance has been proposed. Risk determination and risk criteria are the most important factors in this framework. Risks are determined by FMEA, and new criteria are proposed that are used for decision-making. These criteria are proposed based on practical experience and experts' opinions for the maintenance process in the aeronautic industry. These are evaluated by industrial cases, and this framework capability has been demonstrated.Research limitations/implicationsThe proposed framework and criteria for small unmanned aircraft have been developed based on a practical point of view and expert opinion. Thus for implementation in other aeronautic industries, the framework may need a minor modification.Practical implicationsTwo important subsystems of an unmanned aircraft have been studied, and the capabilities of this method have been presented.Originality/valueThis research is original work to determine a maintenance program for unmanned aircraft that their","PeriodicalId":16938,"journal":{"name":"Journal of Quality in Maintenance Engineering","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41876170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-24DOI: 10.1108/jqme-02-2022-0010
Ronghua Cai, Jiamei Yang, Xuemin Xu, Aiping Jiang
PurposeThe purpose of this paper is to propose an improved multi-objective optimization model for the condition-based maintenance (CBM) of single-component systems which considers periodic imperfect maintenance and ecological factors.Design/methodology/approachBased on the application of non-periodic preventive CBM, two recursion models are built for the system: hazard rate and the environmental degradation factor. This paper also established an optimal multi-objective model with a normalization process. The multiple-attribute value theory is used to obtain the optimal preventive maintenance (PM) interval. The simulation and sensitivity analyses are applied to obtain further rules.FindingsAn increase in the number of the occurrences could shorten the duration of a maintenance cycle. The maintenance techniques and maintenance efficiency could be improved by increasing system availability, reducing cost rate and improving degraded condition.Practical implicationsIn reality, a variety of environmental situations may occur subsequent to the operations of an advanced manufacturing system. This model could be applied in real cases to help the manufacturers better discover the optimal maintenance cycle with minimized cost and degraded condition of the environment, helping the corporations better fulfill their CSR as well.Originality/valuePrevious research on single-component condition-based predictive maintenance usually focused on the maintenance costs and availability of a system, while ignoring the possible pollution from system operations. This paper proposed a modified multi-objective optimization model considering environment influence which could more comprehensively analyze the factors affecting PM interval.
{"title":"A predictive multi-objective condition-based maintenance (CBM) policy considering ecological factors","authors":"Ronghua Cai, Jiamei Yang, Xuemin Xu, Aiping Jiang","doi":"10.1108/jqme-02-2022-0010","DOIUrl":"https://doi.org/10.1108/jqme-02-2022-0010","url":null,"abstract":"PurposeThe purpose of this paper is to propose an improved multi-objective optimization model for the condition-based maintenance (CBM) of single-component systems which considers periodic imperfect maintenance and ecological factors.Design/methodology/approachBased on the application of non-periodic preventive CBM, two recursion models are built for the system: hazard rate and the environmental degradation factor. This paper also established an optimal multi-objective model with a normalization process. The multiple-attribute value theory is used to obtain the optimal preventive maintenance (PM) interval. The simulation and sensitivity analyses are applied to obtain further rules.FindingsAn increase in the number of the occurrences could shorten the duration of a maintenance cycle. The maintenance techniques and maintenance efficiency could be improved by increasing system availability, reducing cost rate and improving degraded condition.Practical implicationsIn reality, a variety of environmental situations may occur subsequent to the operations of an advanced manufacturing system. This model could be applied in real cases to help the manufacturers better discover the optimal maintenance cycle with minimized cost and degraded condition of the environment, helping the corporations better fulfill their CSR as well.Originality/valuePrevious research on single-component condition-based predictive maintenance usually focused on the maintenance costs and availability of a system, while ignoring the possible pollution from system operations. This paper proposed a modified multi-objective optimization model considering environment influence which could more comprehensively analyze the factors affecting PM interval.","PeriodicalId":16938,"journal":{"name":"Journal of Quality in Maintenance Engineering","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48757993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-23DOI: 10.1108/jqme-12-2021-0096
R. Stefanini, Giovanni Paolo Carlo Tancredi, G. Vignali, L. Monica
PurposeIn the context of the Industry 4.0, this paper aims to investigate the state of the art of Italian manufacturing, focusing the attention on the implementation of intelligent predictive maintenance (IPdM) and 4.0 key enabling technologies (KETs), analyzing advantages and limitations encountered by companies.Design/methodology/approachA survey has been developed by the University of Parma in cooperation with the Italian Workers' Compensation Authority (INAIL) and was submitted to a sample of Italian companies. Overall, 70 answers were collected and analyzed.FindingsResults show that the 54% of companies implemented smart technologies, increasing quality and safety, reducing the operating costs and sometimes improving the process' sustainability. However, IPdM was implemented only by the 37% of respondents: thanks to big data collection and analytics, Internet of Things, machine learning and collaborative robots, they reduced downtime and maintenance costs. These changes were implemented mainly by large companies, located in northern Italy. To spread the use of IPdM in Italian manufacturing, the high initial investment, lack of skilled labor and difficulties in the integration of new digital technologies with the existing infrastructure are the main obstacles to overcome.Originality/valueThe article gives an overview on the current state of the art of 4.0 technologies implementation in Italy: it is useful not only for companies that want to discover the implementations' advantages but also for institutions or research centres that could help them to solve the encountered obstacles.
{"title":"Industry 4.0 and intelligent predictive maintenance: a survey about the advantages and constraints in the Italian context","authors":"R. Stefanini, Giovanni Paolo Carlo Tancredi, G. Vignali, L. Monica","doi":"10.1108/jqme-12-2021-0096","DOIUrl":"https://doi.org/10.1108/jqme-12-2021-0096","url":null,"abstract":"PurposeIn the context of the Industry 4.0, this paper aims to investigate the state of the art of Italian manufacturing, focusing the attention on the implementation of intelligent predictive maintenance (IPdM) and 4.0 key enabling technologies (KETs), analyzing advantages and limitations encountered by companies.Design/methodology/approachA survey has been developed by the University of Parma in cooperation with the Italian Workers' Compensation Authority (INAIL) and was submitted to a sample of Italian companies. Overall, 70 answers were collected and analyzed.FindingsResults show that the 54% of companies implemented smart technologies, increasing quality and safety, reducing the operating costs and sometimes improving the process' sustainability. However, IPdM was implemented only by the 37% of respondents: thanks to big data collection and analytics, Internet of Things, machine learning and collaborative robots, they reduced downtime and maintenance costs. These changes were implemented mainly by large companies, located in northern Italy. To spread the use of IPdM in Italian manufacturing, the high initial investment, lack of skilled labor and difficulties in the integration of new digital technologies with the existing infrastructure are the main obstacles to overcome.Originality/valueThe article gives an overview on the current state of the art of 4.0 technologies implementation in Italy: it is useful not only for companies that want to discover the implementations' advantages but also for institutions or research centres that could help them to solve the encountered obstacles.","PeriodicalId":16938,"journal":{"name":"Journal of Quality in Maintenance Engineering","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41564074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-18DOI: 10.1108/jqme-01-2022-0004
Hany Osman, S. Yacout
PurposeIn this paper, a data mining approach is proposed for monitoring the conditions leading to a rail wheel high impact load. The proposed approach incorporates logical analysis of data (LAD) and ant colony optimization (ACO) algorithms in extracting patterns of high impact loads and normal loads from historical railway records. In addition, the patterns are employed in establishing a classification model used for classifying unseen observations. A case study representing real-world impact load data is presented to illustrate the impact of the proposed approach in improving railway services.Design/methodology/approachApplication of artificial intelligence and machine learning approaches becomes an essential tool in improving the performance of railway transportation systems. By using these approaches, the knowledge extracted from historical data can be employed in railway assets monitoring to maintain the assets in a reliable state and to improve the service provided by the railway network.FindingsResults achieved by the proposed approach provide a prognostic system used for monitoring the conditions surrounding rail wheels. Incorporating this prognostic system in surveilling the rail wheels indeed results in better railway services as trips with no-delay or no-failure can be realized. A comparative study is conducted to evaluate the performance of the proposed approach versus other classification algorithms. In addition to the highly interpretable results obtained by the generated patterns, the comparative study demonstrates that the proposed approach provides classification accuracy higher than other common machine learning classification algorithms.Originality/valueThe methodology followed in this research employs ACO algorithm as an artificial intelligent technique and LDA as a machine learning algorithm in analyzing wheel impact load alarm-collected datasets. This new methodology provided a promising classification model to predict future alarm and a prognostic system to guide the system while avoiding this alarm.
{"title":"Condition-based monitoring of the rail wheel using logical analysis of data and ant colony optimization","authors":"Hany Osman, S. Yacout","doi":"10.1108/jqme-01-2022-0004","DOIUrl":"https://doi.org/10.1108/jqme-01-2022-0004","url":null,"abstract":"PurposeIn this paper, a data mining approach is proposed for monitoring the conditions leading to a rail wheel high impact load. The proposed approach incorporates logical analysis of data (LAD) and ant colony optimization (ACO) algorithms in extracting patterns of high impact loads and normal loads from historical railway records. In addition, the patterns are employed in establishing a classification model used for classifying unseen observations. A case study representing real-world impact load data is presented to illustrate the impact of the proposed approach in improving railway services.Design/methodology/approachApplication of artificial intelligence and machine learning approaches becomes an essential tool in improving the performance of railway transportation systems. By using these approaches, the knowledge extracted from historical data can be employed in railway assets monitoring to maintain the assets in a reliable state and to improve the service provided by the railway network.FindingsResults achieved by the proposed approach provide a prognostic system used for monitoring the conditions surrounding rail wheels. Incorporating this prognostic system in surveilling the rail wheels indeed results in better railway services as trips with no-delay or no-failure can be realized. A comparative study is conducted to evaluate the performance of the proposed approach versus other classification algorithms. In addition to the highly interpretable results obtained by the generated patterns, the comparative study demonstrates that the proposed approach provides classification accuracy higher than other common machine learning classification algorithms.Originality/valueThe methodology followed in this research employs ACO algorithm as an artificial intelligent technique and LDA as a machine learning algorithm in analyzing wheel impact load alarm-collected datasets. This new methodology provided a promising classification model to predict future alarm and a prognostic system to guide the system while avoiding this alarm.","PeriodicalId":16938,"journal":{"name":"Journal of Quality in Maintenance Engineering","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41351357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-16DOI: 10.1108/jqme-06-2021-0043
N. Herath, C. Duffield, Lihai Zhang
PurposeSchool infrastructure is one of critical factors that significantly contribute to the educational outcomes, and therefore, maintaining the high quality of school infrastructure becomes of critical importance. Due to the ageing of school assets over time in combination with budget constraint and rapid growth of student enrolment, many public schools are currently struggling to maintain the required standard for long term. However, to date, the goal of providing the best maintenance practices to public schools has not been achieved.Design/methodology/approachThe present study focuses on studying the balance between the asset and maintenance management strategies and the funding model through conducting state-of-the-art literature review and qualitative analysis in the context of public schools in Australia and other developed countries around the world. Review of journal articles, different government reports and other available resources were used to collect and analyse the data in this study.FindingsAs part of this review, significant under investment in maintenance and asset renewals were identified as main challenges in asset management in public school facilities. Although different maintenance strategies were used in school infrastructure, adequate funding, adequate robust asset management plans (AMPs) and the involvement of private sectors have been identified as the key factors that govern the success in school infrastructure maintenance. It also shows that funding of approximately 2–3% of asset replacement value (ARV) on school infrastructure is required to maintain school facilities for long-term. Further, the procurement methods such as public private partnership including private finance initiatives (PFIs) have shown great improvements in maintenance process in school infrastructure.Originality/valueThe study provides a review of different AMPs and funding models in school infrastructure and their efficiencies and shortcoming in detail. Different states and countries use different maintenance models, and challenges associated with each model were also discussed. Further this study also provides some conclusive evidence for better maintenance performance for school buildings.
{"title":"Public-school infrastructure ageing and current challenges in maintenance","authors":"N. Herath, C. Duffield, Lihai Zhang","doi":"10.1108/jqme-06-2021-0043","DOIUrl":"https://doi.org/10.1108/jqme-06-2021-0043","url":null,"abstract":"PurposeSchool infrastructure is one of critical factors that significantly contribute to the educational outcomes, and therefore, maintaining the high quality of school infrastructure becomes of critical importance. Due to the ageing of school assets over time in combination with budget constraint and rapid growth of student enrolment, many public schools are currently struggling to maintain the required standard for long term. However, to date, the goal of providing the best maintenance practices to public schools has not been achieved.Design/methodology/approachThe present study focuses on studying the balance between the asset and maintenance management strategies and the funding model through conducting state-of-the-art literature review and qualitative analysis in the context of public schools in Australia and other developed countries around the world. Review of journal articles, different government reports and other available resources were used to collect and analyse the data in this study.FindingsAs part of this review, significant under investment in maintenance and asset renewals were identified as main challenges in asset management in public school facilities. Although different maintenance strategies were used in school infrastructure, adequate funding, adequate robust asset management plans (AMPs) and the involvement of private sectors have been identified as the key factors that govern the success in school infrastructure maintenance. It also shows that funding of approximately 2–3% of asset replacement value (ARV) on school infrastructure is required to maintain school facilities for long-term. Further, the procurement methods such as public private partnership including private finance initiatives (PFIs) have shown great improvements in maintenance process in school infrastructure.Originality/valueThe study provides a review of different AMPs and funding models in school infrastructure and their efficiencies and shortcoming in detail. Different states and countries use different maintenance models, and challenges associated with each model were also discussed. Further this study also provides some conclusive evidence for better maintenance performance for school buildings.","PeriodicalId":16938,"journal":{"name":"Journal of Quality in Maintenance Engineering","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43807292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-05DOI: 10.1108/jqme-11-2021-0088
M. Saini, Deepak Sinwar, Alapati Manas Swarith, Ashish Kumar
PurposeReliability and maintainability estimation of any system depends on the identification of the best-fitted probability distribution of failure and repair rates. The parameters of the best-fitted probability distribution are also contributing significantly to reliability estimation. In this work, a case study of load haul dump (LHD) machines is illustrated that consider the optimization of failure and repair rate parameters using two well established metaheuristic approaches, namely, genetic algorithm (GA) and particle swarm optimization (PSO). This paper aims to analyze the aforementioned points.Design/methodology/approachThe data on time between failures (TBF) and time to repairs (TTR) are collected for a LHD machine. The descriptive statistical analysis of TBF & TTR data is performed, trend and serial correlation tested and using Anderson–Darling (AD) value best-fitted distributions are identified for repair and failure times of various subsystems. The traditional methods of estimation like maximum likelihood estimation, method of moments, least-square estimation method help only in finding the local solution. Here, for finding the global solution two well-known metaheuristic approaches are applied.FindingsThe reliability of the LHD machine after 60 days on the real data set is 28.55%, using GA on 250 generations is 17.64%, and using PSO on 100 generations and 100 iterations is 30.25%. The PSO technique gives the global best value of reliability.Practical implicationsThe present work will be very convenient for reliability engineers, researchers and maintenance managers to understand the failure and repair pattern of LHD machines. The same methodology can be applied in other process industries also.Originality/valueIn this case study, initially likelihood function of the best-fitted distribution is optimized by GA and PSO. Reliability and maintainability of LHD machines evaluated by the traditional approach, GA and PSO are compared. These results will be very helpful for maintenance engineers to plan new maintenance strategies for better functioning of LHD machines.
{"title":"Reliability and maintainability optimization of load haul dump machines using genetic algorithm and particle swarm optimization","authors":"M. Saini, Deepak Sinwar, Alapati Manas Swarith, Ashish Kumar","doi":"10.1108/jqme-11-2021-0088","DOIUrl":"https://doi.org/10.1108/jqme-11-2021-0088","url":null,"abstract":"PurposeReliability and maintainability estimation of any system depends on the identification of the best-fitted probability distribution of failure and repair rates. The parameters of the best-fitted probability distribution are also contributing significantly to reliability estimation. In this work, a case study of load haul dump (LHD) machines is illustrated that consider the optimization of failure and repair rate parameters using two well established metaheuristic approaches, namely, genetic algorithm (GA) and particle swarm optimization (PSO). This paper aims to analyze the aforementioned points.Design/methodology/approachThe data on time between failures (TBF) and time to repairs (TTR) are collected for a LHD machine. The descriptive statistical analysis of TBF & TTR data is performed, trend and serial correlation tested and using Anderson–Darling (AD) value best-fitted distributions are identified for repair and failure times of various subsystems. The traditional methods of estimation like maximum likelihood estimation, method of moments, least-square estimation method help only in finding the local solution. Here, for finding the global solution two well-known metaheuristic approaches are applied.FindingsThe reliability of the LHD machine after 60 days on the real data set is 28.55%, using GA on 250 generations is 17.64%, and using PSO on 100 generations and 100 iterations is 30.25%. The PSO technique gives the global best value of reliability.Practical implicationsThe present work will be very convenient for reliability engineers, researchers and maintenance managers to understand the failure and repair pattern of LHD machines. The same methodology can be applied in other process industries also.Originality/valueIn this case study, initially likelihood function of the best-fitted distribution is optimized by GA and PSO. Reliability and maintainability of LHD machines evaluated by the traditional approach, GA and PSO are compared. These results will be very helpful for maintenance engineers to plan new maintenance strategies for better functioning of LHD machines.","PeriodicalId":16938,"journal":{"name":"Journal of Quality in Maintenance Engineering","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47910457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-13DOI: 10.1108/jqme-01-2022-0009
Salih Tekin, K. Bicakci, Ozgur Mersin, Gulnur Neval Erdem, Abdulkerim Canbay, Y. Uzunay
PurposeWith the irresistible growth in digitization, data backup policies become essential more than ever for organizations seeking to improve reliability and availability of organizations' information systems. However, since backup operations do not come free, there is a need for a data-informed policy to decide how often and which type of backups should be taken. In this paper, the authors present a comprehensive mathematical framework to explore the design space for backup policies and to optimize backup type and interval in a given system. In the authors' framework, three separate cost factors related to the backup process are identified: backup cost, recovery cost and data loss cost. The objective function has a multi-criteria structure leading to a backup policy minimizing a weighed function of these factors. To formalize the cost and objective functions, the authors get help from renewal theory in reliability modeling. The authors' optimization framework also formulates mixed policies involving both full and incremental backups. Through numerical examples, the authors show how the authors' optimization framework could facilitate cost-saving backup policies.Design/methodology/approachThe methodology starts with designing different backup policies based on system parameters. Each constructed policy is optimized in terms of backup period using renewal theory. After selecting the best back-up policy, the results are demonstrated through numerical studies.FindingsData backup polices that are tailored to system parameters can result in significant gains for IT (Information Technology) systems. Collecting the necessary parameters to design intelligent backup policies can also help managers understand managers' systems better. Designed policies not only provides the frequency of back up operations, but also the type of backups.Originality/valueThe original contribution of this study is the explicit construction and determination of the best backup policies for IT systems that are prone to failure. By applying renewal theory in reliability, the authors present a mathematical framework for the joint optimization of backup cost factors, i.e. backup cost, recovery time cost and data loss cost.
{"title":"Optimal data backup policies for information systems subject to sudden failure","authors":"Salih Tekin, K. Bicakci, Ozgur Mersin, Gulnur Neval Erdem, Abdulkerim Canbay, Y. Uzunay","doi":"10.1108/jqme-01-2022-0009","DOIUrl":"https://doi.org/10.1108/jqme-01-2022-0009","url":null,"abstract":"PurposeWith the irresistible growth in digitization, data backup policies become essential more than ever for organizations seeking to improve reliability and availability of organizations' information systems. However, since backup operations do not come free, there is a need for a data-informed policy to decide how often and which type of backups should be taken. In this paper, the authors present a comprehensive mathematical framework to explore the design space for backup policies and to optimize backup type and interval in a given system. In the authors' framework, three separate cost factors related to the backup process are identified: backup cost, recovery cost and data loss cost. The objective function has a multi-criteria structure leading to a backup policy minimizing a weighed function of these factors. To formalize the cost and objective functions, the authors get help from renewal theory in reliability modeling. The authors' optimization framework also formulates mixed policies involving both full and incremental backups. Through numerical examples, the authors show how the authors' optimization framework could facilitate cost-saving backup policies.Design/methodology/approachThe methodology starts with designing different backup policies based on system parameters. Each constructed policy is optimized in terms of backup period using renewal theory. After selecting the best back-up policy, the results are demonstrated through numerical studies.FindingsData backup polices that are tailored to system parameters can result in significant gains for IT (Information Technology) systems. Collecting the necessary parameters to design intelligent backup policies can also help managers understand managers' systems better. Designed policies not only provides the frequency of back up operations, but also the type of backups.Originality/valueThe original contribution of this study is the explicit construction and determination of the best backup policies for IT systems that are prone to failure. By applying renewal theory in reliability, the authors present a mathematical framework for the joint optimization of backup cost factors, i.e. backup cost, recovery time cost and data loss cost.","PeriodicalId":16938,"journal":{"name":"Journal of Quality in Maintenance Engineering","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41712276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-21DOI: 10.1108/jqme-05-2021-0039
B. Ogunbayo, C. Aigbavboa, W. Thwala, Opeoluwa Israel Opeoluwa, D. Edwards
PurposeMaintenance policy is an element of building maintenance management that deals with organisation policy, planning and procedures, and delineates how maintenance units in an organisation will manage specific building components, auxiliary facilities and services. Given this contextual setting, this study investigates whether organisational maintenance policies (OMPs) utilised in developed countries are relevant in developing countries – using Nigeria as a case study exemplar.Design/methodology/approachAn empirical research design (using deductive reasoning) was implemented for this research. Specifically, a Delphi study conducted revealed 23 elements that impact OMP development in Nigeria.FindingsOf these twenty elements, six had a very high impact on maintenance management (VHI: 9.00–10.00), nine variables had a high impact (HI: 7.00–8.99) and eight other variables scored a medium impact (MI: 5.00–6.99). Emergent findings reveal that the elements of organisational maintenance policy that engender effective building maintenance management include preparation of safety procedure, optimisation of the maintenance policy, optimisation of the maintenance action plan, well-defined priority system, risk factor establishment, suitable maintenance procedures and a clearly delineated process.Practical implicationsThe study findings will guide policymakers in identifying the main elements required in maintenance policies development towards making national public asset preservation and economic gains. Also, the content of the future educational curriculum on maintenance management study will be more receptive to the body of knowledge and the built environment industry.Originality/valueCumulatively, the research presented illustrates that these elements replicate those adopted in other countries and that effective maintenance management of public buildings is assured when these elements are integral to the development of an OMP.
{"title":"Validating elements of organisational maintenance policy for maintenance management of public buildings in Nigeria","authors":"B. Ogunbayo, C. Aigbavboa, W. Thwala, Opeoluwa Israel Opeoluwa, D. Edwards","doi":"10.1108/jqme-05-2021-0039","DOIUrl":"https://doi.org/10.1108/jqme-05-2021-0039","url":null,"abstract":"PurposeMaintenance policy is an element of building maintenance management that deals with organisation policy, planning and procedures, and delineates how maintenance units in an organisation will manage specific building components, auxiliary facilities and services. Given this contextual setting, this study investigates whether organisational maintenance policies (OMPs) utilised in developed countries are relevant in developing countries – using Nigeria as a case study exemplar.Design/methodology/approachAn empirical research design (using deductive reasoning) was implemented for this research. Specifically, a Delphi study conducted revealed 23 elements that impact OMP development in Nigeria.FindingsOf these twenty elements, six had a very high impact on maintenance management (VHI: 9.00–10.00), nine variables had a high impact (HI: 7.00–8.99) and eight other variables scored a medium impact (MI: 5.00–6.99). Emergent findings reveal that the elements of organisational maintenance policy that engender effective building maintenance management include preparation of safety procedure, optimisation of the maintenance policy, optimisation of the maintenance action plan, well-defined priority system, risk factor establishment, suitable maintenance procedures and a clearly delineated process.Practical implicationsThe study findings will guide policymakers in identifying the main elements required in maintenance policies development towards making national public asset preservation and economic gains. Also, the content of the future educational curriculum on maintenance management study will be more receptive to the body of knowledge and the built environment industry.Originality/valueCumulatively, the research presented illustrates that these elements replicate those adopted in other countries and that effective maintenance management of public buildings is assured when these elements are integral to the development of an OMP.","PeriodicalId":16938,"journal":{"name":"Journal of Quality in Maintenance Engineering","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47964834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-13DOI: 10.1108/jqme-07-2021-0056
J. K. Agergaard, K. V. Sigsgaard, N. Mortensen, J. Ge, K. B. Hansen
PurposeThe purpose of this paper is to investigate the impact of early-stage maintenance clustering. Few researchers have previously studied early-stage maintenance clustering. Experience from product and service development has shown that early stages are critical to the development process, as most decisions are made during these stages. Similarly, most maintenance decisions are made during the early stages of maintenance development. Developing maintenance for clustering is expected to increase the potential of clustering.Design/methodology/approachA literature study and three case studies using the same data set were performed. The case studies simulate three stages of maintenance development by clustering based on the changes available at each given stage.FindingsThe study indicates an increased impact of maintenance clustering when clustering already in the first maintenance development stage. By performing clustering during the identification phase, 4.6% of the planned work hours can be saved. When clustering is done in the planning phase, 2.7% of the planned work hours can be saved. When planning is done in the scheduling phase, 2.4% of the planned work hours can be saved. The major difference in potential from the identification to the scheduling phase came from avoiding duplicate, unnecessary and erroneous work.Originality/valueThe findings from this study indicate a need for more studies on early-stage maintenance clustering, as few others have studied this.
{"title":"Quantifying the impact of early-stage maintenance clustering","authors":"J. K. Agergaard, K. V. Sigsgaard, N. Mortensen, J. Ge, K. B. Hansen","doi":"10.1108/jqme-07-2021-0056","DOIUrl":"https://doi.org/10.1108/jqme-07-2021-0056","url":null,"abstract":"PurposeThe purpose of this paper is to investigate the impact of early-stage maintenance clustering. Few researchers have previously studied early-stage maintenance clustering. Experience from product and service development has shown that early stages are critical to the development process, as most decisions are made during these stages. Similarly, most maintenance decisions are made during the early stages of maintenance development. Developing maintenance for clustering is expected to increase the potential of clustering.Design/methodology/approachA literature study and three case studies using the same data set were performed. The case studies simulate three stages of maintenance development by clustering based on the changes available at each given stage.FindingsThe study indicates an increased impact of maintenance clustering when clustering already in the first maintenance development stage. By performing clustering during the identification phase, 4.6% of the planned work hours can be saved. When clustering is done in the planning phase, 2.7% of the planned work hours can be saved. When planning is done in the scheduling phase, 2.4% of the planned work hours can be saved. The major difference in potential from the identification to the scheduling phase came from avoiding duplicate, unnecessary and erroneous work.Originality/valueThe findings from this study indicate a need for more studies on early-stage maintenance clustering, as few others have studied this.","PeriodicalId":16938,"journal":{"name":"Journal of Quality in Maintenance Engineering","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43996303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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