Pub Date : 2023-01-11DOI: 10.1177/1748006x221142815
Murat Ozkut
This paper is about the reliability modeling of a linear consecutive k-out-of- n system that consists of two types of dependent components. The survival function and mean time to failure of such a system are expressed using copulas. Extensive numerical findings are provided for Clayton and Gumbel-type copulas. The survival and mean time to failure behaviors are explored in connection with the value of Kendall’s correlation coefficient.
{"title":"Reliability assessment of consecutive k-out-of-n systems with two types of dependent components","authors":"Murat Ozkut","doi":"10.1177/1748006x221142815","DOIUrl":"https://doi.org/10.1177/1748006x221142815","url":null,"abstract":"This paper is about the reliability modeling of a linear consecutive k-out-of- n system that consists of two types of dependent components. The survival function and mean time to failure of such a system are expressed using copulas. Extensive numerical findings are provided for Clayton and Gumbel-type copulas. The survival and mean time to failure behaviors are explored in connection with the value of Kendall’s correlation coefficient.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84254018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-22DOI: 10.1177/1748006x221140445
H. Zied, R. Nidhal, Kammoun Mohamed Ali, Bouzouba Maryem
This paper studies and proposes a novel joint policy of production, imperfect and priority maintenance for a wind turbine system connected to battery storage and supply grid. The failure rate of wind farm is closely related to production rate and working time. The objective of this paper is to establish an economical energy production and imperfect maintenance plans minimizing the various costs incurred, taking into account the electricity demand variation, uncertainty of wind velocity, and the service level. The proposed maintenance strategy based on the priority and selective maintenance actions aims to choose the priority components for maintenance, while minimizing the total maintenance cost and ensuring a minimum reliability level for the wind turbine system. To achieve the latter goal, we formulate the reliability model of the wind turbine components by considering the influence of operating and environmental conditions. Numerical examples and sensitivity analyzes are presented to illustrate the significance and the effectiveness of the proposed methodology.
{"title":"Improved maintenance strategy for the wind turbine system under operating and climatic conditions","authors":"H. Zied, R. Nidhal, Kammoun Mohamed Ali, Bouzouba Maryem","doi":"10.1177/1748006x221140445","DOIUrl":"https://doi.org/10.1177/1748006x221140445","url":null,"abstract":"This paper studies and proposes a novel joint policy of production, imperfect and priority maintenance for a wind turbine system connected to battery storage and supply grid. The failure rate of wind farm is closely related to production rate and working time. The objective of this paper is to establish an economical energy production and imperfect maintenance plans minimizing the various costs incurred, taking into account the electricity demand variation, uncertainty of wind velocity, and the service level. The proposed maintenance strategy based on the priority and selective maintenance actions aims to choose the priority components for maintenance, while minimizing the total maintenance cost and ensuring a minimum reliability level for the wind turbine system. To achieve the latter goal, we formulate the reliability model of the wind turbine components by considering the influence of operating and environmental conditions. Numerical examples and sensitivity analyzes are presented to illustrate the significance and the effectiveness of the proposed methodology.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90592530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-20DOI: 10.1177/1748006X221140966
Beatriz Sales da Cunha, M. das Chagas Moura, Caio Souto Maior, Ana Cláudia Negreiros, Isis Didier Lins
Systems subjected to continuous operation are exposed to different failure mechanisms such as fatigue, corrosion, and temperature-related defects, which makes inspection and monitoring their health paramount to prevent a system suffering from severe damage. However, visual inspection strongly depends on a human being’s experience, and so its accuracy is influenced by the physical and cognitive state of the inspector. Particularly, civil infrastructures need to be periodically inspected. This is costly, time-consuming, labor-intensive, hazardous, and biased. Advances in Computer Vision (CV) techniques provide the means to develop automated, accurate, non-contact, and non-destructive inspection methods. Hence, this paper compares two different approaches to detecting cracks in images automatically. The first is based on a traditional CV technique, using texture analysis and machine learning methods (TA + ML-based), and the second is based on deep learning (DL), using Convolutional Neural Networks (CNN) models. We analyze both approaches, comparing several ML models and CNN architectures in a real crack database considering six distinct dataset sizes. The results showed that for small-sized datasets, for example, up to 100 images, the DL-based approach achieved a balanced accuracy (BA) of ∼74%, while the TA + ML-based approach obtained a BA > 95%. For larger datasets, the performances of both approaches present comparable results. For images classified as having crack(s), we also evaluate three metrics to measure the severity of a crack based on a segmented version of the original image, as an additional metric to trigger the appropriate maintenance response.
{"title":"A comparison between computer vision- and deep learning-based models for automated concrete crack detection","authors":"Beatriz Sales da Cunha, M. das Chagas Moura, Caio Souto Maior, Ana Cláudia Negreiros, Isis Didier Lins","doi":"10.1177/1748006X221140966","DOIUrl":"https://doi.org/10.1177/1748006X221140966","url":null,"abstract":"Systems subjected to continuous operation are exposed to different failure mechanisms such as fatigue, corrosion, and temperature-related defects, which makes inspection and monitoring their health paramount to prevent a system suffering from severe damage. However, visual inspection strongly depends on a human being’s experience, and so its accuracy is influenced by the physical and cognitive state of the inspector. Particularly, civil infrastructures need to be periodically inspected. This is costly, time-consuming, labor-intensive, hazardous, and biased. Advances in Computer Vision (CV) techniques provide the means to develop automated, accurate, non-contact, and non-destructive inspection methods. Hence, this paper compares two different approaches to detecting cracks in images automatically. The first is based on a traditional CV technique, using texture analysis and machine learning methods (TA + ML-based), and the second is based on deep learning (DL), using Convolutional Neural Networks (CNN) models. We analyze both approaches, comparing several ML models and CNN architectures in a real crack database considering six distinct dataset sizes. The results showed that for small-sized datasets, for example, up to 100 images, the DL-based approach achieved a balanced accuracy (BA) of ∼74%, while the TA + ML-based approach obtained a BA > 95%. For larger datasets, the performances of both approaches present comparable results. For images classified as having crack(s), we also evaluate three metrics to measure the severity of a crack based on a segmented version of the original image, as an additional metric to trigger the appropriate maintenance response.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74661160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-20DOI: 10.1177/1748006x221140487
Ruicong Zhang, Yu Bao, Qinle Weng, Zhongtian Li, YongGang Li
Over the past few years, cross-domain fault detection methods based on unsupervised domain adaptation (UDA) have gradually matured. However, existing methods usually assume that the source and target domains have the same label domain space, but ignore the problem of label expansion in the target domain. The source domain of such problems lacks transferable knowledge of newly added health categories, so the domain invariant features extracted by the UDA model only have a large correlation with the source domain health categories, but lack the key features to distinguish the newly added health categories. We found that most of the diagnostic results of this type of samples are distributed at the decision boundary of the source domain health category, and this special distribution means that the newly added health category samples have a high amount of information. Therefore, this paper considers using active learning to select samples of newly added health categories in the target domain to assist model training, and proposes an active domain adaptation intelligent fault detection framework LDE-ADA to deal with the label expansion problem. Finally, on the rotating machinery dataset, the analysis and comparison are carried out through six transfer tasks. The results show that when there is one new health category, the accuracy of LDE-ADA will increase by about 9.39% in the case of labeling three samples per round and training for 20 rounds. Experiments show that this method is an effective method to deal with the label expansion problem.
{"title":"Active domain adaptation method for label expansion problem","authors":"Ruicong Zhang, Yu Bao, Qinle Weng, Zhongtian Li, YongGang Li","doi":"10.1177/1748006x221140487","DOIUrl":"https://doi.org/10.1177/1748006x221140487","url":null,"abstract":"Over the past few years, cross-domain fault detection methods based on unsupervised domain adaptation (UDA) have gradually matured. However, existing methods usually assume that the source and target domains have the same label domain space, but ignore the problem of label expansion in the target domain. The source domain of such problems lacks transferable knowledge of newly added health categories, so the domain invariant features extracted by the UDA model only have a large correlation with the source domain health categories, but lack the key features to distinguish the newly added health categories. We found that most of the diagnostic results of this type of samples are distributed at the decision boundary of the source domain health category, and this special distribution means that the newly added health category samples have a high amount of information. Therefore, this paper considers using active learning to select samples of newly added health categories in the target domain to assist model training, and proposes an active domain adaptation intelligent fault detection framework LDE-ADA to deal with the label expansion problem. Finally, on the rotating machinery dataset, the analysis and comparison are carried out through six transfer tasks. The results show that when there is one new health category, the accuracy of LDE-ADA will increase by about 9.39% in the case of labeling three samples per round and training for 20 rounds. Experiments show that this method is an effective method to deal with the label expansion problem.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83342135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-20DOI: 10.1177/1748006x221140196
Dario Valcamonico, P. Baraldi, Francesco Amigoni, E. Zio
Road safety analysis is typically performed by domain experts on the basis of the information contained in accident reports. The main challenges are the difficulty of considering a large number of reports in textual form and the subjectivity of the expert judgments contained in reports. This work develops a framework based on the combination of Natural Language Processing (NLP) and Machine Learning (ML) for the automatic classification of accidents with the final aim of assisting experts in performing road safety analyses. Two different models for the representation of the textual reports (Hierarchical Dirichlet Processes (HDPs) and Doc2vec) and three ML-based classifiers (Artificial Neural Networks (ANNs), Decision Trees (DTs) and Random Forests (RFs)) are compared. The framework is applied to a repository of road accident reports provided by the US National Highway Traffic Safety Administration. The best trade-off between accuracy of the classification and explainability of the obtained results is achieved by combining HDP topic modeling and RF classification.
{"title":"A framework based on Natural Language Processing and Machine Learning for the classification of the severity of road accidents from reports","authors":"Dario Valcamonico, P. Baraldi, Francesco Amigoni, E. Zio","doi":"10.1177/1748006x221140196","DOIUrl":"https://doi.org/10.1177/1748006x221140196","url":null,"abstract":"Road safety analysis is typically performed by domain experts on the basis of the information contained in accident reports. The main challenges are the difficulty of considering a large number of reports in textual form and the subjectivity of the expert judgments contained in reports. This work develops a framework based on the combination of Natural Language Processing (NLP) and Machine Learning (ML) for the automatic classification of accidents with the final aim of assisting experts in performing road safety analyses. Two different models for the representation of the textual reports (Hierarchical Dirichlet Processes (HDPs) and Doc2vec) and three ML-based classifiers (Artificial Neural Networks (ANNs), Decision Trees (DTs) and Random Forests (RFs)) are compared. The framework is applied to a repository of road accident reports provided by the US National Highway Traffic Safety Administration. The best trade-off between accuracy of the classification and explainability of the obtained results is achieved by combining HDP topic modeling and RF classification.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78063628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-12DOI: 10.1177/1748006X221138996
Xiaoyue Wang, Ru Ning, Xian Zhao
The reliability of various systems under shock models has been explored extensively in the literature, where the constant mechanism of shock impact has always been studied. Nevertheless, engineering systems operate in a more complicated shock environment due to developments, and it is worth studying the reliability of such multi-component systems in a variable shock environment. Driven by the research gaps and practical situations, this paper constructs a reliability model of multi-component systems under a generalized mixed shock model with a change point. The multi-state components operate in the shock environment I initially and it may continue to run in the shock environment II after the random change point of the environment. Two novel shock impact mechanisms of the variable environment are put forward, where a series of failure criteria based on shocks are included. Four different structures of multi-component systems are considered in this paper. It can be proved that the proposed mixed shock model is a generalization of some transformed models. A multi-stage finite Markov chain imbedding approach is established to derive the probabilistic indices of the components and entire systems. Based on the engineering applications, illustrative examples are provided to verify the effectiveness of the proposed model.
{"title":"Generalized mixed shock model for multi-component systems in the shock environment with a change point","authors":"Xiaoyue Wang, Ru Ning, Xian Zhao","doi":"10.1177/1748006X221138996","DOIUrl":"https://doi.org/10.1177/1748006X221138996","url":null,"abstract":"The reliability of various systems under shock models has been explored extensively in the literature, where the constant mechanism of shock impact has always been studied. Nevertheless, engineering systems operate in a more complicated shock environment due to developments, and it is worth studying the reliability of such multi-component systems in a variable shock environment. Driven by the research gaps and practical situations, this paper constructs a reliability model of multi-component systems under a generalized mixed shock model with a change point. The multi-state components operate in the shock environment I initially and it may continue to run in the shock environment II after the random change point of the environment. Two novel shock impact mechanisms of the variable environment are put forward, where a series of failure criteria based on shocks are included. Four different structures of multi-component systems are considered in this paper. It can be proved that the proposed mixed shock model is a generalization of some transformed models. A multi-stage finite Markov chain imbedding approach is established to derive the probabilistic indices of the components and entire systems. Based on the engineering applications, illustrative examples are provided to verify the effectiveness of the proposed model.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80311691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent decades, many attempts have been made to establish the cause-effect relationship model of accidents, while little work has been carried out to comprehensively consider the interdependence between the causal factors and their complex interactions with the accident outcomes. In this study, a novel accident analysis approach based on Bayesian networks (BNs) was proposed to achieve quantitative accident analysis and dynamic risk prediction of accident types and consequences. To develop the BN-based accident analysis model, a total of 1144 accident cases occurred in tank farm of China from 1960 to 2018 were collected. The BN model that can comprehensively characterize the dependencies among accident elements was established through structural learning based on accident case analysis and parameter learning based on EM algorithm. The reliability and validity of the BN model were verified by k-fold cross-validation method and comparison of predicted data with real data, and the results showed that the BN model had good classification and prediction performance. Furthermore, the established BN model was applied to the accident occurred in Huangdao, China. The analysis results show that not only the accident outcome can be accurately predicted, but also the hidden correlation can be deeply explored through the established BN model. The proposed method and findings can provide technical reference for accident investigation and analysis, and provide decision support for accident prevention and risk management.
{"title":"Accident analysis and risk prediction of tank farm based on Bayesian network method","authors":"Xingguang Wu, Huirong Huang, Weichao Yu, Yuming Lin, Yanhui Xue, Qingwen Cai, Jili Xu","doi":"10.1177/1748006x221139906","DOIUrl":"https://doi.org/10.1177/1748006x221139906","url":null,"abstract":"In recent decades, many attempts have been made to establish the cause-effect relationship model of accidents, while little work has been carried out to comprehensively consider the interdependence between the causal factors and their complex interactions with the accident outcomes. In this study, a novel accident analysis approach based on Bayesian networks (BNs) was proposed to achieve quantitative accident analysis and dynamic risk prediction of accident types and consequences. To develop the BN-based accident analysis model, a total of 1144 accident cases occurred in tank farm of China from 1960 to 2018 were collected. The BN model that can comprehensively characterize the dependencies among accident elements was established through structural learning based on accident case analysis and parameter learning based on EM algorithm. The reliability and validity of the BN model were verified by k-fold cross-validation method and comparison of predicted data with real data, and the results showed that the BN model had good classification and prediction performance. Furthermore, the established BN model was applied to the accident occurred in Huangdao, China. The analysis results show that not only the accident outcome can be accurately predicted, but also the hidden correlation can be deeply explored through the established BN model. The proposed method and findings can provide technical reference for accident investigation and analysis, and provide decision support for accident prevention and risk management.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73647206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-10DOI: 10.1177/1748006x221140194
Plínio MS Ramos, J. Macedo, Caio BS Maior, M. Moura, I. Lins
The occurrence of work accidents may threaten the workers’ health and lead to consequences for the organizations as well, such as restructuring of work and direct/indirect costs with the absence of the worker. In this context, accident investigation reports contain information that can support companies to propose preventive and mitigative measures and identify causes and consequences of injury events. However, this information is frequently complex, redundant, and/or incomplete. Additionally, a complete human review of the entire database is arduous, considering numerous reports produced by a company. Indeed, Natural Language Processing (NLP)-based techniques are suitable for analyzing a massive amount of textual information. In this paper, we adopted NLP techniques to determine whether an injury leave would be expected from a given accident report. The methodology was applied to accident reports collected from an actual hydroelectric power company using Bidirectional Encoder Representations from Transformers (BERT), a state-of-art NLP method. The text representations provided by BERT model were combined with numerical and binary variables extracted from the accident reports. These combined variables are input to a Multilayer Perceptron (MLP) that predicts the occurrence of the accident leave for a given accident. After cross-validation, the results showed a median accuracy of 73.5%. Additionally, we discuss several reports that presented high and low proportions of correct classifications by the models tested and discussed the possible reasons. Indeed, accident investigation reports provide useful knowledge to support decisions in the safety context.
{"title":"Combining BERT with numerical variables to classify injury leave based on accident description","authors":"Plínio MS Ramos, J. Macedo, Caio BS Maior, M. Moura, I. Lins","doi":"10.1177/1748006x221140194","DOIUrl":"https://doi.org/10.1177/1748006x221140194","url":null,"abstract":"The occurrence of work accidents may threaten the workers’ health and lead to consequences for the organizations as well, such as restructuring of work and direct/indirect costs with the absence of the worker. In this context, accident investigation reports contain information that can support companies to propose preventive and mitigative measures and identify causes and consequences of injury events. However, this information is frequently complex, redundant, and/or incomplete. Additionally, a complete human review of the entire database is arduous, considering numerous reports produced by a company. Indeed, Natural Language Processing (NLP)-based techniques are suitable for analyzing a massive amount of textual information. In this paper, we adopted NLP techniques to determine whether an injury leave would be expected from a given accident report. The methodology was applied to accident reports collected from an actual hydroelectric power company using Bidirectional Encoder Representations from Transformers (BERT), a state-of-art NLP method. The text representations provided by BERT model were combined with numerical and binary variables extracted from the accident reports. These combined variables are input to a Multilayer Perceptron (MLP) that predicts the occurrence of the accident leave for a given accident. After cross-validation, the results showed a median accuracy of 73.5%. Additionally, we discuss several reports that presented high and low proportions of correct classifications by the models tested and discussed the possible reasons. Indeed, accident investigation reports provide useful knowledge to support decisions in the safety context.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76342651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-05DOI: 10.1177/1748006x221133873
Qinglai Dong, Pin Liu, Xu-jie Jia
A common cause failure occurs when two or more elements fail due to a shared cause. The system with such failure often needs different repairmen and a multi-level maintenance strategy. The paper studies the reliability of k-out-of-n: G repairable systems considering common cause failure and multi-level maintenance strategy. The models with a multi-level maintenance strategy are established by considering the failure of all and partial components due to common cause failure. The reliability indices of the systems, such as availability and the mean time to first failure, are derived. An optimization maintenance model is established by minimizing the cost, and a three-level maintenance strategy is determined. Numerical examples are provided to illustrate the application. The results show that the multi-level maintenance strategy improves the system reliability and reduces the maintenance costs of systems with common cause failure.
{"title":"Reliability analysis of k-out-of-n: G repairable systems considering common cause failure and multi-level maintenance strategy","authors":"Qinglai Dong, Pin Liu, Xu-jie Jia","doi":"10.1177/1748006x221133873","DOIUrl":"https://doi.org/10.1177/1748006x221133873","url":null,"abstract":"A common cause failure occurs when two or more elements fail due to a shared cause. The system with such failure often needs different repairmen and a multi-level maintenance strategy. The paper studies the reliability of k-out-of-n: G repairable systems considering common cause failure and multi-level maintenance strategy. The models with a multi-level maintenance strategy are established by considering the failure of all and partial components due to common cause failure. The reliability indices of the systems, such as availability and the mean time to first failure, are derived. An optimization maintenance model is established by minimizing the cost, and a three-level maintenance strategy is determined. Numerical examples are provided to illustrate the application. The results show that the multi-level maintenance strategy improves the system reliability and reduces the maintenance costs of systems with common cause failure.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85979890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1177/1748006X211052732
Runxia Guo, Zihang Wang
Any minor fault may spread, accumulate and enlarge through the causal link of fault in a closed-loop complex system of civil aircraft, and eventually result in unplanned downtime. In this paper, the fault propagation path model (FPPM) is proposed for system-level decomposition and simplifying the process of fault propagation analysis by combining the improved ant colony optimization algorithm (I-ACO) with the Bayesian network (BN). In FPPM, the modeling of the fault propagation path consists of three models, namely shrinking model (SM), ant colony optimization model (ACOM), and assessment model (AM). Firstly, the state space is shrunk by the most weight supported tree algorithm (MWST) at the initial establishment stage of BN. Next, I-ACO is designed to improve the structure of BN in order to study the fault propagation path accurately. Finally, the experiment is conducted from two different perspectives for the rationality of the well-trained BN’s structure. An example of practical application for the propagation path model of typical faults on the A320 air turbine starter is given to verify the validity and feasibility of the proposed method.
{"title":"A framework for modeling fault propagation paths in air turbine starter based on Bayesian network","authors":"Runxia Guo, Zihang Wang","doi":"10.1177/1748006X211052732","DOIUrl":"https://doi.org/10.1177/1748006X211052732","url":null,"abstract":"Any minor fault may spread, accumulate and enlarge through the causal link of fault in a closed-loop complex system of civil aircraft, and eventually result in unplanned downtime. In this paper, the fault propagation path model (FPPM) is proposed for system-level decomposition and simplifying the process of fault propagation analysis by combining the improved ant colony optimization algorithm (I-ACO) with the Bayesian network (BN). In FPPM, the modeling of the fault propagation path consists of three models, namely shrinking model (SM), ant colony optimization model (ACOM), and assessment model (AM). Firstly, the state space is shrunk by the most weight supported tree algorithm (MWST) at the initial establishment stage of BN. Next, I-ACO is designed to improve the structure of BN in order to study the fault propagation path accurately. Finally, the experiment is conducted from two different perspectives for the rationality of the well-trained BN’s structure. An example of practical application for the propagation path model of typical faults on the A320 air turbine starter is given to verify the validity and feasibility of the proposed method.","PeriodicalId":51266,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers Part O-Journal of Risk and Reliability","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86807866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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