Pub Date : 2022-11-30DOI: 10.1177/1748006X221136317
A. Raza, V. Ulansky
This study proposes mathematical models for assessing the probabilistic indicators of imperfect inspections conducted when performing condition-based and predictive maintenance. The inspections used in mentioned types of maintenance differ in decision rules regarding system operability at the time of checkup. Contrary to the previous studies, we present the decision rule for each type of inspection on the time axis, which allows the formulation of the set of mutually exclusive events at discrete times. The correct and incorrect decisions correspond to true-positive, false-positive, true-negative, and false-negative events. We propose general expressions for computing the probabilities of possible decisions for both types of inspection. The paper introduces the effectiveness indicators of condition-based and predictive maintenance such as average operating costs, total error probability, and a posteriori probability of failure-free operation. We illustrate the developed approach by calculating the probabilities of correct and incorrect decisions using a specific stochastic deterioration process. The results of the calculations verify that probabilities of correct and incorrect decisions for both types of inspection are very substantially time-dependent despite the large number of published studies where these probabilities are independent of time.
{"title":"Probabilistic indicators of imperfect inspections used in modeling condition-based and predictive maintenance","authors":"A. Raza, V. Ulansky","doi":"10.1177/1748006X221136317","DOIUrl":"https://doi.org/10.1177/1748006X221136317","url":null,"abstract":"This study proposes mathematical models for assessing the probabilistic indicators of imperfect inspections conducted when performing condition-based and predictive maintenance. The inspections used in mentioned types of maintenance differ in decision rules regarding system operability at the time of checkup. Contrary to the previous studies, we present the decision rule for each type of inspection on the time axis, which allows the formulation of the set of mutually exclusive events at discrete times. The correct and incorrect decisions correspond to true-positive, false-positive, true-negative, and false-negative events. We propose general expressions for computing the probabilities of possible decisions for both types of inspection. The paper introduces the effectiveness indicators of condition-based and predictive maintenance such as average operating costs, total error probability, and a posteriori probability of failure-free operation. We illustrate the developed approach by calculating the probabilities of correct and incorrect decisions using a specific stochastic deterioration process. The results of the calculations verify that probabilities of correct and incorrect decisions for both types of inspection are very substantially time-dependent despite the large number of published studies where these probabilities are independent of time.","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":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87466940","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 : 2022-11-30DOI: 10.1177/1748006x221139618
Baoji Yin, Mingjun Zhang, Jiahui Zhou, Wenxian Tang, Z. Jin
This article investigates a novel fault identification approach to determine the percentage of the thrust loss for autonomous underwater vehicle thrusters. The novel approach is developed from a combination of the peak region energy (PRE) and support vector data description (SVDD) by considering that PRE is able to acquire a primary feature in low dimensions from signals without any secondary process and that SVDD can establish a hypersphere boundary for a class of fault samples even in the case of a small number of training samples. Three improvements, namely removing the fusion, an energy leakage and a homomorphic transform are applied to the PRE. It forms an improved PRE to increase the area under the curve. Furthermore, another three new contents, namely the least square, a multiple model fusion and a dead zone are added to the SVDD. It constructs a multiple model least square SVDD to increase the overall identification accuracy. Experiments are performed on an experimental prototype autonomous underwater vehicle in a pool. The experimental results indicate the effectiveness of the proposed method.
{"title":"Thruster fault identification using improved peak region energy and multiple model least square support vector data description for autonomous underwater vehicle","authors":"Baoji Yin, Mingjun Zhang, Jiahui Zhou, Wenxian Tang, Z. Jin","doi":"10.1177/1748006x221139618","DOIUrl":"https://doi.org/10.1177/1748006x221139618","url":null,"abstract":"This article investigates a novel fault identification approach to determine the percentage of the thrust loss for autonomous underwater vehicle thrusters. The novel approach is developed from a combination of the peak region energy (PRE) and support vector data description (SVDD) by considering that PRE is able to acquire a primary feature in low dimensions from signals without any secondary process and that SVDD can establish a hypersphere boundary for a class of fault samples even in the case of a small number of training samples. Three improvements, namely removing the fusion, an energy leakage and a homomorphic transform are applied to the PRE. It forms an improved PRE to increase the area under the curve. Furthermore, another three new contents, namely the least square, a multiple model fusion and a dead zone are added to the SVDD. It constructs a multiple model least square SVDD to increase the overall identification accuracy. Experiments are performed on an experimental prototype autonomous underwater vehicle in a pool. The experimental results indicate the effectiveness 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":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77291320","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 : 2022-11-30DOI: 10.1177/1748006X221134132
Franck Corset, M. Fouladirad, C. Paroissin
A system subject to degradation is considered. The degradation is modelled by a gamma process. A condition-based maintenance policy with perfect corrective and an imperfect preventive actions is proposed. The maintenance cost is derived considering a Markov-renewal process. The statistical inference of the degradation and maintenance parameters by the maximum likelihood method is investigated. A sensibility analysis to different parameters is carried out and the perspectives are detailed.
{"title":"Imperfect condition-based maintenance for a gamma degradation process in presence of unknown parameters","authors":"Franck Corset, M. Fouladirad, C. Paroissin","doi":"10.1177/1748006X221134132","DOIUrl":"https://doi.org/10.1177/1748006X221134132","url":null,"abstract":"A system subject to degradation is considered. The degradation is modelled by a gamma process. A condition-based maintenance policy with perfect corrective and an imperfect preventive actions is proposed. The maintenance cost is derived considering a Markov-renewal process. The statistical inference of the degradation and maintenance parameters by the maximum likelihood method is investigated. A sensibility analysis to different parameters is carried out and the perspectives are detailed.","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":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82513144","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 : 2022-11-30DOI: 10.1177/1748006x221138132
Rongyao Song, Tong Yan, Xiaoyi Wang, Wenxuan Wang
There are inevitably a large number of uncertainties in the actual engineering structures. How to measure the degree of influence of the uncertainty of input variables on structural response is an important issue in structural design. Global sensitivity analysis is an effective means of addressing this problem, in which, the non-probabilistic reliability sensitivity analysis method has received more attention because it is not restricted by the distribution type of random variables. However, the non-probabilistic importance analysis method requires optimization analysis to obtain the extreme values of the performance function, resulting in its application in practical engineering problems being somewhat limited. To address this problem, this paper firstly proposed an efficient optimization method based on the high-dimensional model decomposition and Taylor expansion series combined with the quadratic function; Secondly, the non-probabilistic reliability importance analysis method is improved based on the proposed optimization method; Finally, two numerical cases are utilized to illustrate the accuracy and efficiency of the proposed method, and an engineering example is used to illustrate the engineering practicality of the proposed method. It was found that regardless of the value of the safety threshold, it affects only the non-probability reliability indicators and has little effect on the magnitude of the non-probability reliability importance indicators and the order of importance of the parameters.
{"title":"An efficient approximate optimization algorithm and its application to non-probabilistic reliability importance measures","authors":"Rongyao Song, Tong Yan, Xiaoyi Wang, Wenxuan Wang","doi":"10.1177/1748006x221138132","DOIUrl":"https://doi.org/10.1177/1748006x221138132","url":null,"abstract":"There are inevitably a large number of uncertainties in the actual engineering structures. How to measure the degree of influence of the uncertainty of input variables on structural response is an important issue in structural design. Global sensitivity analysis is an effective means of addressing this problem, in which, the non-probabilistic reliability sensitivity analysis method has received more attention because it is not restricted by the distribution type of random variables. However, the non-probabilistic importance analysis method requires optimization analysis to obtain the extreme values of the performance function, resulting in its application in practical engineering problems being somewhat limited. To address this problem, this paper firstly proposed an efficient optimization method based on the high-dimensional model decomposition and Taylor expansion series combined with the quadratic function; Secondly, the non-probabilistic reliability importance analysis method is improved based on the proposed optimization method; Finally, two numerical cases are utilized to illustrate the accuracy and efficiency of the proposed method, and an engineering example is used to illustrate the engineering practicality of the proposed method. It was found that regardless of the value of the safety threshold, it affects only the non-probability reliability indicators and has little effect on the magnitude of the non-probability reliability importance indicators and the order of importance of the parameters.","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":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87001420","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 : 2022-11-29DOI: 10.1177/1748006x221133600
Masahiro Sasabe, Miyu Otani, Takanori Hara, S. Kasahara
When nodes and/or links are down in a network, the network may not function normally. Most of the existing work focuses on the reachability between two nodes along a path, that is, path reliability, and that through arbitrary paths, that is, network reliability. However, in case of wireless multi-hop networks and road networks, it may be inefficient or difficult to recalculate a path from the source to the destination when a failure occurs at an intermediate link in the path. In such cases, we can expect that the reachability between two nodes will improve by taking a detour from the entry of the failure link (i.e. failure point) to the destination without traversing the failure link. Since the detour may also increase the communication/travel delay, in this paper, we propose a new path metric (i.e. path reachability including distance-constrained detours), which consists of the conventional path reachability and the reachability along distance-constrained detours under arbitrary link failures in the original path. We first prove the two important characteristics: (1) the proposed metric is exactly the same as the network reliability in case of no distance constraint and (2) it is upper bounded by the diameter constrained network reliability. Through numerical results using a grid network and more realistic networks (i.e. wireless networks and a road network), we show the fundamental characteristics of the proposed metric and analyze the goodness of several representative paths in terms of the proposed metric as well as the conventional metrics (i.e. path length and path reachability).
{"title":"Path reachability including distance-constrained detours","authors":"Masahiro Sasabe, Miyu Otani, Takanori Hara, S. Kasahara","doi":"10.1177/1748006x221133600","DOIUrl":"https://doi.org/10.1177/1748006x221133600","url":null,"abstract":"When nodes and/or links are down in a network, the network may not function normally. Most of the existing work focuses on the reachability between two nodes along a path, that is, path reliability, and that through arbitrary paths, that is, network reliability. However, in case of wireless multi-hop networks and road networks, it may be inefficient or difficult to recalculate a path from the source to the destination when a failure occurs at an intermediate link in the path. In such cases, we can expect that the reachability between two nodes will improve by taking a detour from the entry of the failure link (i.e. failure point) to the destination without traversing the failure link. Since the detour may also increase the communication/travel delay, in this paper, we propose a new path metric (i.e. path reachability including distance-constrained detours), which consists of the conventional path reachability and the reachability along distance-constrained detours under arbitrary link failures in the original path. We first prove the two important characteristics: (1) the proposed metric is exactly the same as the network reliability in case of no distance constraint and (2) it is upper bounded by the diameter constrained network reliability. Through numerical results using a grid network and more realistic networks (i.e. wireless networks and a road network), we show the fundamental characteristics of the proposed metric and analyze the goodness of several representative paths in terms of the proposed metric as well as the conventional metrics (i.e. path length and path reachability).","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":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78483367","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 : 2022-11-27DOI: 10.1177/1748006x221135907
Dooyoul Lee, Hyeok-Jun Kwon, K. Choi
The integrity of an aircraft gas turbine engine is critical for safety of flight. Although the reliability of engines has improved considerably, a large number of legacy engines continue to operate. The maintenance of legacy engines is a major burden for their operators owing to the high cost involved, and the engines pose a high risk to flight safety. In this study, we developed a comprehensive approach for risk-based maintenance optimization of aircraft engine components. The approach involved the use of a physics-informed data-driven model incorporated with the Weibayes model and a simple fatigue crack growth model. The crack length distribution and corresponding risks were evaluated using Bayesian updating and knowledge of nondestructive inspection reliability. The model was used for the computation of the fatigue reliability of the first-stage blisk of a CT7-9C turboprop engine. A single failure was used for the Weibayes analysis, and a master crack growth curve was obtained through quantitative-fractography-based crack growth analysis. Furthermore, an inspection model based on the evaluation of field inspectors was used to obtain the posterior crack length distribution, and through a sensitivity analysis, important factors were identified. Finally, optimal inspection and replacement plans were formulated using approximated objective and constraint functions. In particular, the life-cycle cost was minimized while maintaining the risks within strict limits.
{"title":"Risk-based maintenance optimization of aircraft gas turbine engine component","authors":"Dooyoul Lee, Hyeok-Jun Kwon, K. Choi","doi":"10.1177/1748006x221135907","DOIUrl":"https://doi.org/10.1177/1748006x221135907","url":null,"abstract":"The integrity of an aircraft gas turbine engine is critical for safety of flight. Although the reliability of engines has improved considerably, a large number of legacy engines continue to operate. The maintenance of legacy engines is a major burden for their operators owing to the high cost involved, and the engines pose a high risk to flight safety. In this study, we developed a comprehensive approach for risk-based maintenance optimization of aircraft engine components. The approach involved the use of a physics-informed data-driven model incorporated with the Weibayes model and a simple fatigue crack growth model. The crack length distribution and corresponding risks were evaluated using Bayesian updating and knowledge of nondestructive inspection reliability. The model was used for the computation of the fatigue reliability of the first-stage blisk of a CT7-9C turboprop engine. A single failure was used for the Weibayes analysis, and a master crack growth curve was obtained through quantitative-fractography-based crack growth analysis. Furthermore, an inspection model based on the evaluation of field inspectors was used to obtain the posterior crack length distribution, and through a sensitivity analysis, important factors were identified. Finally, optimal inspection and replacement plans were formulated using approximated objective and constraint functions. In particular, the life-cycle cost was minimized while maintaining the risks within strict limits.","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":"2022-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73564842","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 : 2022-11-27DOI: 10.1177/1748006x221136582
Yang Tang, Xin Yang, Bo Lei, Liu Yang, Chong Xie
To address the problems of the poor feature extraction ability and weak data generalization ability of traditional fault diagnosis methods in reciprocating shale gas compressor fault diagnosis applications, in this study, a fault diagnosis method for reciprocating shale gas was developed. This method uses a novel optimized learning method, free energy in persistent contrastive divergence, in deep belief network learning and training. It solves the problem of the deep belief network classification ability degradation in long-term training. The root mean square error is used as the fitness function to search for the optimal parameter combination of the DBN network by using the sparrow search algorithm. At the same time, the learning rate and batch size of the deep belief network, which have a large impact on the training error, are selected for optimization. Then, the original vibration signal is preprocessed by calculating 13 different time domain indicators, and feature-level data and decision-level data are fused in a parallel superposition method to obtain a fused time domain index dataset. Finally, combined with the powerful adaptive feature extraction and nonlinear mapping ability of deep learning, the constructed sample dataset is input to the deep belief network for training, and the deep belief network based on reciprocating shale gas compressor fault diagnosis model is established.
{"title":"Multisensor information fusion method for intelligent fault diagnosis of reciprocating compressor in shale gas development","authors":"Yang Tang, Xin Yang, Bo Lei, Liu Yang, Chong Xie","doi":"10.1177/1748006x221136582","DOIUrl":"https://doi.org/10.1177/1748006x221136582","url":null,"abstract":"To address the problems of the poor feature extraction ability and weak data generalization ability of traditional fault diagnosis methods in reciprocating shale gas compressor fault diagnosis applications, in this study, a fault diagnosis method for reciprocating shale gas was developed. This method uses a novel optimized learning method, free energy in persistent contrastive divergence, in deep belief network learning and training. It solves the problem of the deep belief network classification ability degradation in long-term training. The root mean square error is used as the fitness function to search for the optimal parameter combination of the DBN network by using the sparrow search algorithm. At the same time, the learning rate and batch size of the deep belief network, which have a large impact on the training error, are selected for optimization. Then, the original vibration signal is preprocessed by calculating 13 different time domain indicators, and feature-level data and decision-level data are fused in a parallel superposition method to obtain a fused time domain index dataset. Finally, combined with the powerful adaptive feature extraction and nonlinear mapping ability of deep learning, the constructed sample dataset is input to the deep belief network for training, and the deep belief network based on reciprocating shale gas compressor fault diagnosis model is established.","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":"2022-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80341513","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 : 2022-11-27DOI: 10.1177/1748006x221135459
M. Naybour, R. Remenyte-Prescott, M. Boyd
There are 11,619 community pharmacies in England which dispense over 1 billion prescriptions each year, providing essential primary care to NHS (National Health Service) patients. These pharmacies are facing pressure from a number of sources including funding cuts and high demands on services, while trying to deliver the highest standards of care. This paper presents an optimisation of a Coloured Petri Net (CPN) community pharmacy simulation model using an Ant Colony Optimisation (ACO) method. The CPN method was proposed by Naybour et al . Quantitative data from UK community pharmacies was collected by the authors and incorporated into the CPN simulation model. The optimisation is made up of a choice of how many staff to employ, which prescription checking strategy to use, and which staff work pattern to implement. This method aims to provide decision makers with a set of optimal pharmacy configurations at different cost levels. This can help to support pharmacy safety, efficiency, and improve decision making processes. It has been demonstrated how reliability modelling techniques traditionally used in safety-critical industries, can be used to carry out safety and efficiency analyses of healthcare systems, such as dispensing processes in community pharmacies, illustrated in this contribution.
{"title":"Ant colony optimisation of a community pharmacy dispensing process using Coloured Petri-Net simulation and UK pharmacy in-field data","authors":"M. Naybour, R. Remenyte-Prescott, M. Boyd","doi":"10.1177/1748006x221135459","DOIUrl":"https://doi.org/10.1177/1748006x221135459","url":null,"abstract":"There are 11,619 community pharmacies in England which dispense over 1 billion prescriptions each year, providing essential primary care to NHS (National Health Service) patients. These pharmacies are facing pressure from a number of sources including funding cuts and high demands on services, while trying to deliver the highest standards of care. This paper presents an optimisation of a Coloured Petri Net (CPN) community pharmacy simulation model using an Ant Colony Optimisation (ACO) method. The CPN method was proposed by Naybour et al . Quantitative data from UK community pharmacies was collected by the authors and incorporated into the CPN simulation model. The optimisation is made up of a choice of how many staff to employ, which prescription checking strategy to use, and which staff work pattern to implement. This method aims to provide decision makers with a set of optimal pharmacy configurations at different cost levels. This can help to support pharmacy safety, efficiency, and improve decision making processes. It has been demonstrated how reliability modelling techniques traditionally used in safety-critical industries, can be used to carry out safety and efficiency analyses of healthcare systems, such as dispensing processes in community pharmacies, illustrated in this contribution.","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":"2022-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83956035","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}
Accurate remaining useful life (RUL) prediction is helpful to improve the reliability and safety of complex systems. However, in practical engineering applications, it often occurs imperfect or scarce prior degradation information for the degradation system with measurement error (ME). In order to solve this problem, based on the implicit linear Wiener degradation process, a RUL prediction method which reasonably fuses failure time data or multi-source information is proposed in this paper. Firstly, based on the implicit linear Wiener degradation process, we obtain the relationship between the natures of parameters estimation and degradation data by theoretical derivation, which provides a theoretical basis regarding how to fuse multi-source information. Secondly, according to the natures of parameters estimation, we use field degradation data and historical degradation data to estimate the fixed parameters of the two prediction cases respectively, and fuse failure time data into the degradation model by the expectation maximization (EM) algorithm. Then, the Kalman filtering algorithm is used to online update the drift parameter based on field degradation data. Finally, we use some simulation experiments to further verify the natures of parameters estimation, and two practical case studies to verify the superiority of the proposed method.
{"title":"Remaining useful life prediction of implicit linear Wiener degradation process based on multi-source information","authors":"Jiaxin Yang, Shengjin Tang, Pengya Fang, Fengfei Wang, Xiaoyan Sun, Xiaosheng Si","doi":"10.1177/1748006x221132606","DOIUrl":"https://doi.org/10.1177/1748006x221132606","url":null,"abstract":"Accurate remaining useful life (RUL) prediction is helpful to improve the reliability and safety of complex systems. However, in practical engineering applications, it often occurs imperfect or scarce prior degradation information for the degradation system with measurement error (ME). In order to solve this problem, based on the implicit linear Wiener degradation process, a RUL prediction method which reasonably fuses failure time data or multi-source information is proposed in this paper. Firstly, based on the implicit linear Wiener degradation process, we obtain the relationship between the natures of parameters estimation and degradation data by theoretical derivation, which provides a theoretical basis regarding how to fuse multi-source information. Secondly, according to the natures of parameters estimation, we use field degradation data and historical degradation data to estimate the fixed parameters of the two prediction cases respectively, and fuse failure time data into the degradation model by the expectation maximization (EM) algorithm. Then, the Kalman filtering algorithm is used to online update the drift parameter based on field degradation data. Finally, we use some simulation experiments to further verify the natures of parameters estimation, and two practical case studies to verify the superiority 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":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89957189","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 : 2022-11-19DOI: 10.1177/1748006x221133866
Xiao Zhu, Y. Ran, Xinglong Li, Liming Xiao
This paper presents a parameter estimation method for the lifetime distribution of the Meta-Action Unit (MAU) with uncertainty failure modes under type-I censored data. The MAU is regarded as the basic functional unit to accomplish the function of mechanical equipment, and its failure modes are classified according to the abnormal kinematic parameters in Meta-Action (MA), which are more succinct than the traditional mechanical failure modes on parts. However, there is some uncertain information about the failure data and censored data of MAU because of the technology limitations and the space accessibility constraints for monitoring the kinematic parameters of MA, which uncertainty information can impact the parameter estimates of MAU lifetime distribution. In order to avoid the impacts on the estimating accuracy of distribution parameters, the evidential likelihood function based on the belief function theory is constructed in view of the credibility level of the failure data and censored data. In addition, the Evidential Expectation Maximization (E2M) algorithm is proposed to estimate the parameters of the mixed exponential distribution of MAU lifetime under type-I censored data. Finally, an application of an Automatic Pallet Changer (APC) is used to illustrate the validity of the MAU failure modes classification. The simulations of the E2M algorithm are conducted to show that the proposed parameters estimation method can integrate uncertain information in the failure data and the censored data, and obtain more stable results than those based on the conventional Expectation-Maximization (EM).
{"title":"Parameter estimation of lifetime distribution for the meta-action unit with uncertainty failure modes under type-I censored data","authors":"Xiao Zhu, Y. Ran, Xinglong Li, Liming Xiao","doi":"10.1177/1748006x221133866","DOIUrl":"https://doi.org/10.1177/1748006x221133866","url":null,"abstract":"This paper presents a parameter estimation method for the lifetime distribution of the Meta-Action Unit (MAU) with uncertainty failure modes under type-I censored data. The MAU is regarded as the basic functional unit to accomplish the function of mechanical equipment, and its failure modes are classified according to the abnormal kinematic parameters in Meta-Action (MA), which are more succinct than the traditional mechanical failure modes on parts. However, there is some uncertain information about the failure data and censored data of MAU because of the technology limitations and the space accessibility constraints for monitoring the kinematic parameters of MA, which uncertainty information can impact the parameter estimates of MAU lifetime distribution. In order to avoid the impacts on the estimating accuracy of distribution parameters, the evidential likelihood function based on the belief function theory is constructed in view of the credibility level of the failure data and censored data. In addition, the Evidential Expectation Maximization (E2M) algorithm is proposed to estimate the parameters of the mixed exponential distribution of MAU lifetime under type-I censored data. Finally, an application of an Automatic Pallet Changer (APC) is used to illustrate the validity of the MAU failure modes classification. The simulations of the E2M algorithm are conducted to show that the proposed parameters estimation method can integrate uncertain information in the failure data and the censored data, and obtain more stable results than those based on the conventional Expectation-Maximization (EM).","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":"2022-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88334609","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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