Pub Date : 2024-09-19DOI: 10.1177/1748006x241272829
Wei Xie, Zhaohan Liu, Kani Fu, Shuneng Zhong
The inventory cost of stocking spare parts is a nonnegligible expenditure of providing after-sales service for the manufacturers making capital-intensive products, such as electric vehicles. Especially, for warranty repair service, it is important to manage the spare stock appropriately to satisfy the warranty claims of customers as well as reduce the associated inventory costs. In this paper, we investigate the spare parts inventory issue related to a critical component for under-warranty units of a product. In particular, under the free-replacement warranty policy, failed component will be replaced by a new one by consuming the spare stock. According to the field claim data, we find that the general trend of warranty claims is nonstationary, which will be affected by the product sales and under-warranty failures. Thus, we first propose a model to forecast the time-varying warranty repair demand by explicitly considering the randomness from two major sources, that is, product sales and under-warranty failures. Under the assumptions of Poisson sales process and exponential failure distribution, the closed-form expressions of mean and variance of cumulative warranty repair demand over time are obtained. Because the number of warranty claims in each period is a one-time data, the associated distribution information is unavailable. Then, based on the properties of the demand statistics, we derived a worst-case upper bound for the associated inventory cost and formulate a three-phase finite-horizon spare parts inventory model, which can be used to appropriately address the time-varying warranty claims. Finally, numerical experiments are conducted to investigate the key parameters affecting the optimal decisions where a case study based on real data is presented.
{"title":"Spare parts provisioning strategy of warranty repair demands for capital-intensive products","authors":"Wei Xie, Zhaohan Liu, Kani Fu, Shuneng Zhong","doi":"10.1177/1748006x241272829","DOIUrl":"https://doi.org/10.1177/1748006x241272829","url":null,"abstract":"The inventory cost of stocking spare parts is a nonnegligible expenditure of providing after-sales service for the manufacturers making capital-intensive products, such as electric vehicles. Especially, for warranty repair service, it is important to manage the spare stock appropriately to satisfy the warranty claims of customers as well as reduce the associated inventory costs. In this paper, we investigate the spare parts inventory issue related to a critical component for under-warranty units of a product. In particular, under the free-replacement warranty policy, failed component will be replaced by a new one by consuming the spare stock. According to the field claim data, we find that the general trend of warranty claims is nonstationary, which will be affected by the product sales and under-warranty failures. Thus, we first propose a model to forecast the time-varying warranty repair demand by explicitly considering the randomness from two major sources, that is, product sales and under-warranty failures. Under the assumptions of Poisson sales process and exponential failure distribution, the closed-form expressions of mean and variance of cumulative warranty repair demand over time are obtained. Because the number of warranty claims in each period is a one-time data, the associated distribution information is unavailable. Then, based on the properties of the demand statistics, we derived a worst-case upper bound for the associated inventory cost and formulate a three-phase finite-horizon spare parts inventory model, which can be used to appropriately address the time-varying warranty claims. Finally, numerical experiments are conducted to investigate the key parameters affecting the optimal decisions where a case study based on real data is presented.","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":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142260930","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 : 2024-09-18DOI: 10.1177/1748006x241271884
Xinyun Zhu, Jianzhong Sun, Zichen Yan, Yutong Xu
The efficacy of system fault diagnosis is intricately linked with the testability design of the system, particularly in intricate systems encompassing numerous components exhibiting diverse failure modes. However, current research shows a dearth of effective testability models and algorithms for generating diagnostic strategies in multi-valued attribute systems (MVAS) with uncertainty. To address this, the present paper introduces a novel method for testability modeling for complex systems. This approach incorporates signal features in lieu of raw sensor signals within the testability modeling process and accounts for the uncertainties surrounding test outcomes. The context of complex MVAS, characterized by inherent uncertainty, the paper proposes a novel method for constructing a four-value dependency matrix (D-matrix). Furthermore, the paper presents a novel sequential diagnosis strategy optimization approach based on a heuristic evaluation function for the multivalued D-matrix with uncertainty. The proposed methodology has been rigorously validated using a real-world case study involving an aero-engine fuel metering device system. Comparative experiments show that the proposed method can achieve better diagnostic performance in the shortest time.
系统故障诊断的有效性与系统的可测试性设计密切相关，尤其是在包含众多组件的复杂系统中，故障模式多种多样。然而，目前的研究表明，在具有不确定性的多值属性系统（MVAS）中，缺乏有效的可测试性模型和算法来生成诊断策略。为解决这一问题，本文介绍了一种用于复杂系统可测试性建模的新方法。这种方法在可测试性建模过程中采用了信号特征来代替原始传感器信号，并考虑了测试结果的不确定性。复杂的 MVAS 具有固有的不确定性，因此本文提出了一种构建四值依赖矩阵（D 矩阵）的新方法。此外，本文还提出了一种基于启发式评估函数的新型顺序诊断策略优化方法，用于具有不确定性的多值 D 矩阵。所提出的方法已通过一项涉及航空发动机燃油计量装置系统的实际案例研究进行了严格验证。对比实验表明，所提出的方法能在最短时间内实现更好的诊断性能。
{"title":"Integrated testability modeling method of complex systems for fault feature selection and diagnosis strategy optimization","authors":"Xinyun Zhu, Jianzhong Sun, Zichen Yan, Yutong Xu","doi":"10.1177/1748006x241271884","DOIUrl":"https://doi.org/10.1177/1748006x241271884","url":null,"abstract":"The efficacy of system fault diagnosis is intricately linked with the testability design of the system, particularly in intricate systems encompassing numerous components exhibiting diverse failure modes. However, current research shows a dearth of effective testability models and algorithms for generating diagnostic strategies in multi-valued attribute systems (MVAS) with uncertainty. To address this, the present paper introduces a novel method for testability modeling for complex systems. This approach incorporates signal features in lieu of raw sensor signals within the testability modeling process and accounts for the uncertainties surrounding test outcomes. The context of complex MVAS, characterized by inherent uncertainty, the paper proposes a novel method for constructing a four-value dependency matrix (D-matrix). Furthermore, the paper presents a novel sequential diagnosis strategy optimization approach based on a heuristic evaluation function for the multivalued D-matrix with uncertainty. The proposed methodology has been rigorously validated using a real-world case study involving an aero-engine fuel metering device system. Comparative experiments show that the proposed method can achieve better diagnostic performance in the shortest 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":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142260931","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 : 2024-09-17DOI: 10.1177/1748006x241271751
Yuanyuan Liu, Mingguang Zhang, Yudie Chang
Chemical laboratories usually have many high risks from various dangerous chemicals, dangerous devices, and experimental operators involved in the laboratory. Once a severe accident occurs, the accident consequence will significantly impact scientific research innovation and social progress. Aiming at laboratory safety problems, an accident evolution model is established to reveal the critical accident-causing factors and the most likely evolutionary path of accidents in the process of accident evolution. Firstly, the 24 model is used to identify the cause factors of laboratory accidents and the causal relationship between factors. Then, the complex network model of laboratory accident evolution is constructed with accident-causing factors as nodes and causal relationships between factors as edges. Secondly, using the TOPSIS method, four typical complex network characteristic indexes, including degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality, are constructed into a comprehensive evaluation index to reveal the key influencing factors in the accident evolution process. Finally, the fuzzy number is introduced to quantify the uncertainty of the accident evolution path. The most likely evolutionary path is obtained through the Dijkstra and risk entropy theory. Based on the data of 151 laboratory accidents from 2001 to 2022, the accident evolution analysis of chemical laboratories was carried out. The evolution results show that the maximum probability value is the evolution path of fire accidents, and the minimum probability value is the evolution path of electric shock accidents, which accords with the general statistical law of accidents. When applied to the actual laboratory, the safety management level of the laboratory can be effectively improved by controlling the key influencing factors and cutting off the accident evolution path.
{"title":"Risk analysis of accident-causing evolution in chemical laboratory based on complex network","authors":"Yuanyuan Liu, Mingguang Zhang, Yudie Chang","doi":"10.1177/1748006x241271751","DOIUrl":"https://doi.org/10.1177/1748006x241271751","url":null,"abstract":"Chemical laboratories usually have many high risks from various dangerous chemicals, dangerous devices, and experimental operators involved in the laboratory. Once a severe accident occurs, the accident consequence will significantly impact scientific research innovation and social progress. Aiming at laboratory safety problems, an accident evolution model is established to reveal the critical accident-causing factors and the most likely evolutionary path of accidents in the process of accident evolution. Firstly, the 24 model is used to identify the cause factors of laboratory accidents and the causal relationship between factors. Then, the complex network model of laboratory accident evolution is constructed with accident-causing factors as nodes and causal relationships between factors as edges. Secondly, using the TOPSIS method, four typical complex network characteristic indexes, including degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality, are constructed into a comprehensive evaluation index to reveal the key influencing factors in the accident evolution process. Finally, the fuzzy number is introduced to quantify the uncertainty of the accident evolution path. The most likely evolutionary path is obtained through the Dijkstra and risk entropy theory. Based on the data of 151 laboratory accidents from 2001 to 2022, the accident evolution analysis of chemical laboratories was carried out. The evolution results show that the maximum probability value is the evolution path of fire accidents, and the minimum probability value is the evolution path of electric shock accidents, which accords with the general statistical law of accidents. When applied to the actual laboratory, the safety management level of the laboratory can be effectively improved by controlling the key influencing factors and cutting off the accident evolution path.","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":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142260932","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 : 2024-09-17DOI: 10.1177/1748006x241272827
Na Wang, Wentao Mao, Yanna Zhang, Panpan Zeng, Zhidan Zhong
As a critical issue of diagnostics and health management (PHM), health indicator (HI) construction aims to describe the degradation process of bearings and can provide essential support of domain knowledge for early fault detection and remaining useful life prediction. In recent years, various deep neural networks, with end-to-end modeling capability, have been successfully applied to the HI construction for rolling bearings. In small-sample environment, however, the degradation features would not be extracted well by deep learning techniques, which may raise insufficient tendency and monotonicity characteristics in the obtained HI sequence. To address this concern, this paper proposes a HI construction method based on wavelet scattering network (WSN) and makes an empirical evaluation from frequency perspective. First, degradation features in different frequency bands are extracted from vibration signals by using WSN to expand the feature space with different scales and orientations. Second, the frequency band with the optimal scale and orientation parameters is selected by calculating the dynamic time wrapping (DTW) distance between the feature sequences of each frequency band and the root mean square (RMS) sequence. With the feature subset from the determined frequency band, the HI sequence can be built by means of principal component analysis (PCA). Experimental results on the IEEE PHM Challenge 2012 bearing dataset show that the proposed method can work well with only a small amount of bearing whole-life data in obtaining the HI sequences with high monotonicity and correlation characteristics. More interestingly, the critical frequency band whose information supports decisively the HI construction can be clarified, raising interpretability in a frequency sense and enhancing the credibility of the obtained HI sequence as well.
作为诊断和健康管理（PHM）的一个关键问题，健康指标（HI）的构建旨在描述轴承的退化过程，并为早期故障检测和剩余使用寿命预测提供必要的领域知识支持。近年来，各种具有端到端建模能力的深度神经网络已成功应用于滚动轴承的健康指标构建。然而，在小样本环境下，深度学习技术无法很好地提取退化特征，这可能会导致所获得的 HI 序列缺乏足够的倾向性和单调性特征。针对这一问题，本文提出了一种基于小波散射网络（WSN）的 HI 构建方法，并从频率角度进行了实证评估。首先，利用小波散射网络从振动信号中提取不同频段的退化特征，从而扩展不同尺度和方向的特征空间。其次，通过计算各频段特征序列与均方根序列之间的动态时间包络（DTW）距离，选择具有最佳比例和方向参数的频段。有了确定频段的特征子集，就可以通过主成分分析（PCA）建立 HI 序列。在 IEEE PHM Challenge 2012 轴承数据集上的实验结果表明，建议的方法只需少量的轴承全寿命数据就能很好地获得具有高单调性和相关性特征的 HI 序列。更有趣的是，该方法还能明确其信息对构建 HI 起决定性作用的临界频段，从而提高频率意义上的可解释性，并增强所获 HI 序列的可信度。
{"title":"Small-sample health indicator construction of rolling bearings with wavelet scattering network: An empirical study from frequency perspective","authors":"Na Wang, Wentao Mao, Yanna Zhang, Panpan Zeng, Zhidan Zhong","doi":"10.1177/1748006x241272827","DOIUrl":"https://doi.org/10.1177/1748006x241272827","url":null,"abstract":"As a critical issue of diagnostics and health management (PHM), health indicator (HI) construction aims to describe the degradation process of bearings and can provide essential support of domain knowledge for early fault detection and remaining useful life prediction. In recent years, various deep neural networks, with end-to-end modeling capability, have been successfully applied to the HI construction for rolling bearings. In small-sample environment, however, the degradation features would not be extracted well by deep learning techniques, which may raise insufficient tendency and monotonicity characteristics in the obtained HI sequence. To address this concern, this paper proposes a HI construction method based on wavelet scattering network (WSN) and makes an empirical evaluation from frequency perspective. First, degradation features in different frequency bands are extracted from vibration signals by using WSN to expand the feature space with different scales and orientations. Second, the frequency band with the optimal scale and orientation parameters is selected by calculating the dynamic time wrapping (DTW) distance between the feature sequences of each frequency band and the root mean square (RMS) sequence. With the feature subset from the determined frequency band, the HI sequence can be built by means of principal component analysis (PCA). Experimental results on the IEEE PHM Challenge 2012 bearing dataset show that the proposed method can work well with only a small amount of bearing whole-life data in obtaining the HI sequences with high monotonicity and correlation characteristics. More interestingly, the critical frequency band whose information supports decisively the HI construction can be clarified, raising interpretability in a frequency sense and enhancing the credibility of the obtained HI sequence as well.","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":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142260933","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}
{"title":"Editoral on special issue “Text mining applied to risk analysis, maintenance and safety”","authors":"Márcio das Chagas Moura, Piero Baraldi, Enrique López Droguett, Enrico Zio","doi":"10.1177/1748006x241280066","DOIUrl":"https://doi.org/10.1177/1748006x241280066","url":null,"abstract":"","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":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208857","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 : 2024-08-31DOI: 10.1177/1748006x241270735
Youcef Sid Amer, Samir Benammar, Kong Fah Tee, Zouhir Iourzikene
Although composite hydrogen tanks are becoming increasingly intriguing, a detailed structural reliability analysis is still lacking. The current landscape of analysis for pressurised multilayer cylinders, while rich in research, lacks tools to deal with the low probabilities of failure that govern the design of hydrogen tanks. This study presents a computational framework integrating the Subset Simulation method (SS) for assessing the failure probability of composite tanks used for hydrogen storage. To explore how randomness impacts design parameters, this study utilises a limit state equation derived from the thin-walled circumferential model of composite pressure tanks. Five random variables, each with varying coefficients of variation (COVs), are incorporated into the analysis. For comparison and validation purposes, two methods Monte Carlo simulations and FORM have been used. The analysis revealed that SS excels at estimating the low failure probabilities of composite hydrogen tanks, and showed also the feasibility and accuracy of SS in the prediction of burst pressure since good agreement was obtained between the probabilistic approach and the experimental results. Furthermore, the uncertainties related to internal pressure and composite thickness affect significantly the reliability of the structure and can lead to the shrinkage of the safety margin and the failure of the vessel.
尽管复合材料氢气罐越来越吸引人，但仍然缺乏详细的结构可靠性分析。目前对加压多层气瓶的分析虽然研究成果丰富，但却缺乏工具来处理氢气罐设计中的低失效概率问题。本研究提出了一个计算框架，该框架集成了子集模拟法（SS），用于评估氢气存储用复合材料储罐的失效概率。为了探索随机性如何影响设计参数，本研究利用了从复合材料压力容器薄壁圆周模型中推导出的极限状态方程。分析中纳入了五个随机变量，每个变量都有不同的变异系数（COV）。为了进行比较和验证，使用了蒙特卡罗模拟和 FORM 两种方法。分析结果表明，SS 在估算复合材料氢气罐的低失效概率方面表现出色，同时还显示了 SS 在预测爆破压力方面的可行性和准确性，因为概率方法与实验结果之间取得了良好的一致。此外，与内部压力和复合材料厚度相关的不确定性会严重影响结构的可靠性，并可能导致安全裕度缩减和容器失效。
{"title":"A study on the small failure probabilities of cylindrical composite hydrogen storage tanks using subset simulation","authors":"Youcef Sid Amer, Samir Benammar, Kong Fah Tee, Zouhir Iourzikene","doi":"10.1177/1748006x241270735","DOIUrl":"https://doi.org/10.1177/1748006x241270735","url":null,"abstract":"Although composite hydrogen tanks are becoming increasingly intriguing, a detailed structural reliability analysis is still lacking. The current landscape of analysis for pressurised multilayer cylinders, while rich in research, lacks tools to deal with the low probabilities of failure that govern the design of hydrogen tanks. This study presents a computational framework integrating the Subset Simulation method (SS) for assessing the failure probability of composite tanks used for hydrogen storage. To explore how randomness impacts design parameters, this study utilises a limit state equation derived from the thin-walled circumferential model of composite pressure tanks. Five random variables, each with varying coefficients of variation (COVs), are incorporated into the analysis. For comparison and validation purposes, two methods Monte Carlo simulations and FORM have been used. The analysis revealed that SS excels at estimating the low failure probabilities of composite hydrogen tanks, and showed also the feasibility and accuracy of SS in the prediction of burst pressure since good agreement was obtained between the probabilistic approach and the experimental results. Furthermore, the uncertainties related to internal pressure and composite thickness affect significantly the reliability of the structure and can lead to the shrinkage of the safety margin and the failure of the vessel.","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":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142208859","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 : 2024-08-03DOI: 10.1177/1748006x241263922
Jingyuan Shen, Jiahui Xu, Yao Duan, Fengxia Zhang, Yizhong Ma
Systems with dependent main and auxiliary components have been extensively investigated in the reliability field recently, but the influence of the changing environment has been less taken into consideration. Motivated by some real applications, when the protective auxiliary component fails, the degradation/failure rate of the main component varies as it is exposed to different environments. To bridge the gap between research and practice, in this paper the influences of the dynamic environments and the component dependencies are both incorporated to develop a new reliability model for systems with main and auxiliary components. A continuous-time homogeneous Markov process is used to model the evolution of the environments. When the auxiliary component works, it could protect the main component from the negative impact of the environment. Once the auxiliary component fails, the main component would degrade at different rates according to different environment states. Based on the proposed model, first the reliability of the system is derived in a recursive way. Besides, an opportunistic inspection and maintenance policy is designed for the system, and some important indexes such as the limiting average availability and the long-run average cost are derived. Finally, through numerical examples, the applicability of the proposed model and sensitivity analysis of the model parameters are discussed.
{"title":"Reliability modelling for systems degrading in Markovian environments with protective auxiliary components","authors":"Jingyuan Shen, Jiahui Xu, Yao Duan, Fengxia Zhang, Yizhong Ma","doi":"10.1177/1748006x241263922","DOIUrl":"https://doi.org/10.1177/1748006x241263922","url":null,"abstract":"Systems with dependent main and auxiliary components have been extensively investigated in the reliability field recently, but the influence of the changing environment has been less taken into consideration. Motivated by some real applications, when the protective auxiliary component fails, the degradation/failure rate of the main component varies as it is exposed to different environments. To bridge the gap between research and practice, in this paper the influences of the dynamic environments and the component dependencies are both incorporated to develop a new reliability model for systems with main and auxiliary components. A continuous-time homogeneous Markov process is used to model the evolution of the environments. When the auxiliary component works, it could protect the main component from the negative impact of the environment. Once the auxiliary component fails, the main component would degrade at different rates according to different environment states. Based on the proposed model, first the reliability of the system is derived in a recursive way. Besides, an opportunistic inspection and maintenance policy is designed for the system, and some important indexes such as the limiting average availability and the long-run average cost are derived. Finally, through numerical examples, the applicability of the proposed model and sensitivity analysis of the model parameters are discussed.","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":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141945481","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 : 2024-07-26DOI: 10.1177/1748006x241259215
Mohammad Pishahang, Andres Ruiz-Tagle, Marilia A. Ramos, Enrique Lopez Droguett, Ali Mosleh
Efficient evacuation of wildfire-threatened communities is a pressing challenge. A reliable evacuation planning and execution requires a comprehensive understanding of the diverse and interdependent physical, social, and behavioral components, and advanced, yet easy to use decision support system. This paper proposes the Wildfire Safe Egress (WiSE) framework, which integrates the fire dynamics, human behavior, and traffic model to predict the chance of safe egress by any given community during a wildfire evacuation. WISE framework presents a unified dependency diagram and workflow offering consistent granularity between sub-models and creates comparable evacuation scenarios. A human behavior model is proposed to predict the community decision making and action based on their socio-demographic vulnerability profile. An agent-based stochastic approach generates evacuation departure times. The travel times are calculated through a congestion-informed traffic simulation. Finally, a Bayesian Network is used to combine the sub-models and to predict community safety (probability of successful evacuation) via probabilistic inference based on the integrated model. A proof-of-concept software implementation of the WiSE framework is also presented. To demonstrate the model and platform capabilities the evacuation of the entire city of Paradise during the California Camp Fire 2018 is simulated. The simulation results are qualitatively validated by the firefighters who served in this disaster. A sensitivity analysis of the parameters is performed to compare several evacuation scenarios and provide insights for future wildfire evacuation plannings.
{"title":"A Bayesian agent-based model and software for wildfire safe evacuation planning and management","authors":"Mohammad Pishahang, Andres Ruiz-Tagle, Marilia A. Ramos, Enrique Lopez Droguett, Ali Mosleh","doi":"10.1177/1748006x241259215","DOIUrl":"https://doi.org/10.1177/1748006x241259215","url":null,"abstract":"Efficient evacuation of wildfire-threatened communities is a pressing challenge. A reliable evacuation planning and execution requires a comprehensive understanding of the diverse and interdependent physical, social, and behavioral components, and advanced, yet easy to use decision support system. This paper proposes the Wildfire Safe Egress (WiSE) framework, which integrates the fire dynamics, human behavior, and traffic model to predict the chance of safe egress by any given community during a wildfire evacuation. WISE framework presents a unified dependency diagram and workflow offering consistent granularity between sub-models and creates comparable evacuation scenarios. A human behavior model is proposed to predict the community decision making and action based on their socio-demographic vulnerability profile. An agent-based stochastic approach generates evacuation departure times. The travel times are calculated through a congestion-informed traffic simulation. Finally, a Bayesian Network is used to combine the sub-models and to predict community safety (probability of successful evacuation) via probabilistic inference based on the integrated model. A proof-of-concept software implementation of the WiSE framework is also presented. To demonstrate the model and platform capabilities the evacuation of the entire city of Paradise during the California Camp Fire 2018 is simulated. The simulation results are qualitatively validated by the firefighters who served in this disaster. A sensitivity analysis of the parameters is performed to compare several evacuation scenarios and provide insights for future wildfire evacuation plannings.","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":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141783948","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 : 2024-07-26DOI: 10.1177/1748006x241264446
Li Jiang, Zhipeng Yu, Kejia Zhuang, Yibing Li
In recent years, convolutional neural network (CNN) has been successfully applied in the field of bearing fault diagnosis. So as to improve the diagnosis performance in harsh environment with strong noise, the structure of CNN-based feature extractor becomes deeper and more complex. However, with the increase of depth, the model may lose shallow features and the training parameters will surge. Moreover, if the sample size is not large, it tends to over fit. It deviates from the concept of network lightweight. On the other hand, little attention will be paid to the optimization of model classifiers which can significantly improve the classification performance. Therefore, we proposed a CNN with full stage optimization (FSOCNN) model for bearing fault diagnosis in strong noise environment. In the feature extraction stage, the model is optimized with a novel multi-feature output structure connected with global average pooling to improve the feature extraction ability without any extra trainable parameters. In the classification stage, the traditional softmax layer will only participate in the parameter optimization of CNN model through gradient descent algorithm, and the diagnosis results will be output by support vector machine. The effectiveness of the proposed method is verified on the two bearing datasets under different levels of noise. Compared with the existing five fault diagnosis models, the results prove that the proposed method possesses higher accuracy, less computing time, and better stability.
{"title":"A novel low-cost bearing fault diagnosis method based on convolutional neural network with full stage optimization in strong noise environment","authors":"Li Jiang, Zhipeng Yu, Kejia Zhuang, Yibing Li","doi":"10.1177/1748006x241264446","DOIUrl":"https://doi.org/10.1177/1748006x241264446","url":null,"abstract":"In recent years, convolutional neural network (CNN) has been successfully applied in the field of bearing fault diagnosis. So as to improve the diagnosis performance in harsh environment with strong noise, the structure of CNN-based feature extractor becomes deeper and more complex. However, with the increase of depth, the model may lose shallow features and the training parameters will surge. Moreover, if the sample size is not large, it tends to over fit. It deviates from the concept of network lightweight. On the other hand, little attention will be paid to the optimization of model classifiers which can significantly improve the classification performance. Therefore, we proposed a CNN with full stage optimization (FSOCNN) model for bearing fault diagnosis in strong noise environment. In the feature extraction stage, the model is optimized with a novel multi-feature output structure connected with global average pooling to improve the feature extraction ability without any extra trainable parameters. In the classification stage, the traditional softmax layer will only participate in the parameter optimization of CNN model through gradient descent algorithm, and the diagnosis results will be output by support vector machine. The effectiveness of the proposed method is verified on the two bearing datasets under different levels of noise. Compared with the existing five fault diagnosis models, the results prove that the proposed method possesses higher accuracy, less computing time, and better stability.","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":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141783944","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 : 2024-07-25DOI: 10.1177/1748006x241259833
Li Nianhuan, Gu Dongwei, Wang Zhiqiong, Wang Juncheng, Li Shuailin, Chen Bingkun, Chen Pengfei
Constant-stress accelerated degradation test (CSADT) is an effective means of evaluating the reliability of products, to ensure accurate assessment of reliability-related indicators under limited funds. The optimized design of CSADT has been widely applied. The advantage of Wiener process in capturing the random nature of non-monotonic degradation paths caused by inherent uncertainties in products has also been widely used in the application of accelerated degradation tests (ADT). To address the drawback of traditional Wiener process with constant diffusion coefficients leading to low accuracy in test evaluation, a multi-stress coupled constant acceleration degradation model with stress-related diffusion coefficients is proposed. This includes the construction of a D-optimization criterion that minimizes the variance of model parameter estimation as the optimization target, and a scheme optimization method based on Particle Swarm Optimization (PSO) with cost constraints. Through the analysis of a case study of ADT for LED lamps, comparison and parameter sensitivity analysis of four accelerated degradation models with or without considering stress-related diffusion coefficients, the effectiveness, and robustness of the model in the paper are validated.
恒应力加速降解试验（CSADT）是评价产品可靠性的有效手段，可确保在有限的资金条件下准确评估可靠性相关指标。CSADT 的优化设计已得到广泛应用。维纳过程在捕捉产品固有不确定性所导致的非单调降解路径的随机性方面的优势也被广泛应用于加速降解试验（ADT）。针对传统维纳过程的扩散系数恒定导致试验评估精度低的缺点，提出了一种与应力相关的扩散系数的多应力耦合恒定加速度降解模型。其中包括构建以模型参数估计方差最小化为优化目标的 D 优化准则，以及基于粒子群优化（PSO）的方案优化方法和成本约束。通过对 LED 灯 ADT 案例研究的分析，对考虑或不考虑应力相关扩散系数的四个加速降解模型进行比较和参数敏感性分析，验证了本文模型的有效性和稳健性。
{"title":"Multiple stresses optimization design of constant-stress accelerated degradation test based on Wiener process","authors":"Li Nianhuan, Gu Dongwei, Wang Zhiqiong, Wang Juncheng, Li Shuailin, Chen Bingkun, Chen Pengfei","doi":"10.1177/1748006x241259833","DOIUrl":"https://doi.org/10.1177/1748006x241259833","url":null,"abstract":"Constant-stress accelerated degradation test (CSADT) is an effective means of evaluating the reliability of products, to ensure accurate assessment of reliability-related indicators under limited funds. The optimized design of CSADT has been widely applied. The advantage of Wiener process in capturing the random nature of non-monotonic degradation paths caused by inherent uncertainties in products has also been widely used in the application of accelerated degradation tests (ADT). To address the drawback of traditional Wiener process with constant diffusion coefficients leading to low accuracy in test evaluation, a multi-stress coupled constant acceleration degradation model with stress-related diffusion coefficients is proposed. This includes the construction of a D-optimization criterion that minimizes the variance of model parameter estimation as the optimization target, and a scheme optimization method based on Particle Swarm Optimization (PSO) with cost constraints. Through the analysis of a case study of ADT for LED lamps, comparison and parameter sensitivity analysis of four accelerated degradation models with or without considering stress-related diffusion coefficients, the effectiveness, and robustness of the model in the paper are validated.","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":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784100","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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