Min Zhang;Jiamin Li;Zhuang Kang;Tong Lan;Haohao Ding
High-speed train brake pads state recognition faces the problems of single data source feature characterization limitation and significant domain shifts under variable working conditions. Considering that multisource heterogeneous data can characterize the brake pad state from different physical dimensions, this article proposes a multisource deep adversarial decoupled autoencoder network for online identification of brake pad state of high-speed trains under variable working conditions. First, a signal characterization system covering the multidimensional state characteristics of the friction interface is constructed by fusing three kinds of multisource heterogeneous data, including friction coefficient, tangential acceleration, and noise. Second, a deep adversarial decoupled autoencoder is designed to realize the explicit decoupling of domain-invariant and domain-specific features by utilizing the synergistic mechanism of mutual information minimization constraint and domain adversarial. Finally, with the validation set accuracy as the optimization objective, a genetic algorithm is introduced to dynamically allocate multisource weights. This adaptive weighted fusion strategy significantly enhances the model’s generalization capability for unknown rotational speed conditions. The experimental results of 10 cross-speed tasks show that the proposed model achieves an average accuracy of 99.12% . It is 7.1%, 9.36%, and 26.5% higher than the single-source model, and 3.58% to 6.36% better than the current leading domain generalization methods.
{"title":"Multisource Deep Adversarial Decoupled Autoencoder Network for State Recognition of High-Speed Train Brake Pads","authors":"Min Zhang;Jiamin Li;Zhuang Kang;Tong Lan;Haohao Ding","doi":"10.1109/TR.2025.3643732","DOIUrl":"https://doi.org/10.1109/TR.2025.3643732","url":null,"abstract":"High-speed train brake pads state recognition faces the problems of single data source feature characterization limitation and significant domain shifts under variable working conditions. Considering that multisource heterogeneous data can characterize the brake pad state from different physical dimensions, this article proposes a multisource deep adversarial decoupled autoencoder network for online identification of brake pad state of high-speed trains under variable working conditions. First, a signal characterization system covering the multidimensional state characteristics of the friction interface is constructed by fusing three kinds of multisource heterogeneous data, including friction coefficient, tangential acceleration, and noise. Second, a deep adversarial decoupled autoencoder is designed to realize the explicit decoupling of domain-invariant and domain-specific features by utilizing the synergistic mechanism of mutual information minimization constraint and domain adversarial. Finally, with the validation set accuracy as the optimization objective, a genetic algorithm is introduced to dynamically allocate multisource weights. This adaptive weighted fusion strategy significantly enhances the model’s generalization capability for unknown rotational speed conditions. The experimental results of 10 cross-speed tasks show that the proposed model achieves an average accuracy of 99.12% . It is 7.1%, 9.36%, and 26.5% higher than the single-source model, and 3.58% to 6.36% better than the current leading domain generalization methods.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"75 ","pages":"639-649"},"PeriodicalIF":5.7,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital twin with generative artificial intelligence (AI)-enabled maintenance optimization serves as an essential foundation for the performance of intelligent manufacturing systems (IMS). However, existing models often fail to simultaneously consider both reliability and cost. In an IMS, reliability guarantees stable system operation and consistent product quality, while cost control enables enterprises to optimize resource use, enhance productivity, and lower operating costs. Together, these metrics determine the overall effectiveness of the system and the competitiveness of the enterprise. To address the research gap, this study proposes a maintenance optimization method that jointly considers reliability and cost. In particular, a novel reliability assessment method is developed, incorporating both physical failures modeled and functional outputs that account for imperfect quality inspection. Moreover, considering rework and imperfect quality inspection, a cost analysis is performed for various operation modes of IMS. Further, a novel adaptive multi-objective particle swarm optimization with maintenance priority constraints (AMOPSO-P) method is developed to conduct the IMS control decision-making process, optimizing reliability and cost. Finally, to validate the proposed algorithm, we conduct a case study of China United Equipment Group on control decisions for a three-stage, four-station servo valve manufacturing system using simulations.
{"title":"Digital Twin-Enabled Smart Operation and Maintenance Framework With Generative AI Design of Intelligent Manufacturing Systems","authors":"Hongyan Dui;Hengbo Wang;Liudong Xing","doi":"10.1109/TR.2025.3646186","DOIUrl":"https://doi.org/10.1109/TR.2025.3646186","url":null,"abstract":"Digital twin with generative artificial intelligence (AI)-enabled maintenance optimization serves as an essential foundation for the performance of intelligent manufacturing systems (IMS). However, existing models often fail to simultaneously consider both reliability and cost. In an IMS, reliability guarantees stable system operation and consistent product quality, while cost control enables enterprises to optimize resource use, enhance productivity, and lower operating costs. Together, these metrics determine the overall effectiveness of the system and the competitiveness of the enterprise. To address the research gap, this study proposes a maintenance optimization method that jointly considers reliability and cost. In particular, a novel reliability assessment method is developed, incorporating both physical failures modeled and functional outputs that account for imperfect quality inspection. Moreover, considering rework and imperfect quality inspection, a cost analysis is performed for various operation modes of IMS. Further, a novel adaptive multi-objective particle swarm optimization with maintenance priority constraints (AMOPSO-P) method is developed to conduct the IMS control decision-making process, optimizing reliability and cost. Finally, to validate the proposed algorithm, we conduct a case study of China United Equipment Group on control decisions for a three-stage, four-station servo valve manufacturing system using simulations.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"75 ","pages":"504-517"},"PeriodicalIF":5.7,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the continuous development of smart grids, the cyber-physical power system (CPPS) has become the core architecture of modern power systems. However, accurately identifying critical nodes in CPPS to guard against cascading failures remains a severe challenge. Existing methods fail to effectively characterize the hierarchical interactions and cannot capture the dynamic characteristics of cascading failure propagation in real time online, thus resorting to offline evaluation approaches. To address this, this article proposes an online identification method for critical nodes in CPPS using a deep reinforcement learning framework, providing a reference for node protection. This method identifies critical nodes from two different perspectives: network topology and node electrical characteristics. First, corresponding feature representations are designed for different types of nodes. Then, a deep learning framework called CP-DQN, which integrates feature perception and topology perception, is constructed by combining graph attention networks and dueling deep Q-network, enabling adaptive fusion of node topological and electrical features. Simulation results show that the proposed method exhibits superior performance in the IEEE 39 and IEEE 118 bus systems. Compared with several existing mainstream methods, it demonstrates higher superiority and practicality.
{"title":"Deep Reinforcement Learning-Based Approach for Identifying Critical Nodes in Cyber Physical Power Systems","authors":"Yuancheng Li;Hefang Zhang","doi":"10.1109/TR.2025.3646881","DOIUrl":"https://doi.org/10.1109/TR.2025.3646881","url":null,"abstract":"With the continuous development of smart grids, the cyber-physical power system (CPPS) has become the core architecture of modern power systems. However, accurately identifying critical nodes in CPPS to guard against cascading failures remains a severe challenge. Existing methods fail to effectively characterize the hierarchical interactions and cannot capture the dynamic characteristics of cascading failure propagation in real time online, thus resorting to offline evaluation approaches. To address this, this article proposes an online identification method for critical nodes in CPPS using a deep reinforcement learning framework, providing a reference for node protection. This method identifies critical nodes from two different perspectives: network topology and node electrical characteristics. First, corresponding feature representations are designed for different types of nodes. Then, a deep learning framework called CP-DQN, which integrates feature perception and topology perception, is constructed by combining graph attention networks and dueling deep Q-network, enabling adaptive fusion of node topological and electrical features. Simulation results show that the proposed method exhibits superior performance in the IEEE 39 and IEEE 118 bus systems. Compared with several existing mainstream methods, it demonstrates higher superiority and practicality.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"75 ","pages":"464-477"},"PeriodicalIF":5.7,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yudong Cao;Junxian Shen;Jichao Zhuang;Xiaoli Zhao;Xiaoan Yan
In the era of big data and intelligent sensing, deep neural networks provide new impetus for prognostics and health management (PHM) with their powerful feature extraction capabilities. However, the pursuit of performance through increased network depth and complexity concurrently escalates the number of hyperparameters and model intricacy, thereby exacerbating the inherent opaque nature and restricting their deployment in complex industrial settings. To address this dilemma, this article develops a machine health prognosis framework with ex-ante interpretability based on complex domain time-frequency network (CDTFN). Specifically, this article first investigates the intrinsic bonds between network convolution and time-frequency transforms. Building upon this foundation, four complex observation operators with trainable parameters are designed for extracting fault-related time-frequency information, embedding it into the CDTFN as a preprocessing layer. Simultaneously, by extending the forward and backward propagation mechanisms of real-valued networks to the complex domain, the proposed CDTFN gains the capability to fuse complex-valued time-frequency information and establish end-to-end mapping from feature representation layers to prediction labels. The effectiveness and accuracy of the proposed prognosis framework based on CDTFN are verified by public and self-built run-to-failure rolling bearings datasets. The detailed experimental results further demonstrate its distinct advantages in interpretability and generalization capability.
{"title":"Investigation of Bonds Between Network Convolution and Time-Frequency Transforms for Ex-Ante Interpretable Machine Health Prognosis","authors":"Yudong Cao;Junxian Shen;Jichao Zhuang;Xiaoli Zhao;Xiaoan Yan","doi":"10.1109/TR.2025.3647116","DOIUrl":"https://doi.org/10.1109/TR.2025.3647116","url":null,"abstract":"In the era of big data and intelligent sensing, deep neural networks provide new impetus for prognostics and health management (PHM) with their powerful feature extraction capabilities. However, the pursuit of performance through increased network depth and complexity concurrently escalates the number of hyperparameters and model intricacy, thereby exacerbating the inherent opaque nature and restricting their deployment in complex industrial settings. To address this dilemma, this article develops a machine health prognosis framework with ex-ante interpretability based on complex domain time-frequency network (CDTFN). Specifically, this article first investigates the intrinsic bonds between network convolution and time-frequency transforms. Building upon this foundation, four complex observation operators with trainable parameters are designed for extracting fault-related time-frequency information, embedding it into the CDTFN as a preprocessing layer. Simultaneously, by extending the forward and backward propagation mechanisms of real-valued networks to the complex domain, the proposed CDTFN gains the capability to fuse complex-valued time-frequency information and establish end-to-end mapping from feature representation layers to prediction labels. The effectiveness and accuracy of the proposed prognosis framework based on CDTFN are verified by public and self-built run-to-failure rolling bearings datasets. The detailed experimental results further demonstrate its distinct advantages in interpretability and generalization capability.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"75 ","pages":"490-503"},"PeriodicalIF":5.7,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Juan Xu;Haoyu He;Xu Ding;Qile Ren;Mingguang Dai;Yongbin Zhang
Few-shot learning (FSL) techniques have been introduced to address the challenge of limited datasets in mechanical fault diagnosis. However, most existing FSL methods primarily focus on input–output correlations and neglect causal relationships, which limits the interpretability and robustness of diagnostic results. To tackle this issue, we propose a causal disentanglement few-shot relation metric network for mechanical fault diagnosis, comprising feature encoding, causal intervention, causal disentanglement, and relation metric modules. The causal intervention module performs linear interpolation on amplitude information (encoding low-level statistics) while preserving phase information (encoding high-level semantics) to intervene causally on the frequency-domain image. Fault features are extracted via the feature encoder module, and a factor disentanglement loss in the causal disentanglement module transforms them into independent causal features with explicit causal relationships. The relation metric module learns pairwise causal feature distances through meta-task training, thus constructing a trainable similarity metric space. This approach can effectively capture the differences in causal fault features between samples, enhancing the interpretability and generalization ability of the model. Experiments on both public and laboratory datasets demonstrate superior performance over state-of-the-art methods.
{"title":"CDRNet: A Causality Disentanglement Few-Shot Mechanical Fault Diagnosis","authors":"Juan Xu;Haoyu He;Xu Ding;Qile Ren;Mingguang Dai;Yongbin Zhang","doi":"10.1109/TR.2025.3637129","DOIUrl":"https://doi.org/10.1109/TR.2025.3637129","url":null,"abstract":"Few-shot learning (FSL) techniques have been introduced to address the challenge of limited datasets in mechanical fault diagnosis. However, most existing FSL methods primarily focus on input–output correlations and neglect causal relationships, which limits the interpretability and robustness of diagnostic results. To tackle this issue, we propose a causal disentanglement few-shot relation metric network for mechanical fault diagnosis, comprising feature encoding, causal intervention, causal disentanglement, and relation metric modules. The causal intervention module performs linear interpolation on amplitude information (encoding low-level statistics) while preserving phase information (encoding high-level semantics) to intervene causally on the frequency-domain image. Fault features are extracted via the feature encoder module, and a factor disentanglement loss in the causal disentanglement module transforms them into independent causal features with explicit causal relationships. The relation metric module learns pairwise causal feature distances through meta-task training, thus constructing a trainable similarity metric space. This approach can effectively capture the differences in causal fault features between samples, enhancing the interpretability and generalization ability of the model. Experiments on both public and laboratory datasets demonstrate superior performance over state-of-the-art methods.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"75 ","pages":"319-332"},"PeriodicalIF":5.7,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145847762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Remaining useful life (RUL) prediction is vital for the safety of engineering assets. In the real scenario, due to the lack of failure data and variable working conditions, the accuracy of predictive RUL is significantly compromised as models struggle to generalize across diverse operating environments. Existing solutions manage to shift the degradation information from the ideal laboratory environment to the complex real-world environment. However, they fail to consider the heterogeneity of operating machines under different working conditions. This ignorance of inherent properties will eventually hamper the accuracy of RUL prediction. Consequently, a novel Bayesian adversarial Fast Linear Attention with a Single Head (FLASH) Transformer with feature disentanglement model (BAFTFD) was proposed in this article to tackle with the problem. The proposed BAFTFD model can disentangle the private feature representations from the raw data, preserving the shared feature representation for the prediction. The adversarial training method is also exploited to facilitate the transfer of degradation knowledge. Besides, the feature extractor is equipped with the effective FLASH Transformer model to retain the most informative degradation features for model training, improving the efficiency of feature extraction. Moreover, considering the impact of insufficient training data, inherent data noise on the trustworthiness of the predictive results, the Bayesian DL method is adopted to quantify the prediction uncertainties, ensuring the reliability of maintenance decisions. Two commercial turbofan datasets are leveraged to validate the designed model.
{"title":"Bayesian Adversarial Adaptation Network With Feature Disentanglement for Remaining Useful Life Prediction","authors":"Yongbo Cheng;Junheng Qv;Liangqi Wan;Te Han","doi":"10.1109/TR.2025.3626149","DOIUrl":"https://doi.org/10.1109/TR.2025.3626149","url":null,"abstract":"Remaining useful life (RUL) prediction is vital for the safety of engineering assets. In the real scenario, due to the lack of failure data and variable working conditions, the accuracy of predictive RUL is significantly compromised as models struggle to generalize across diverse operating environments. Existing solutions manage to shift the degradation information from the ideal laboratory environment to the complex real-world environment. However, they fail to consider the heterogeneity of operating machines under different working conditions. This ignorance of inherent properties will eventually hamper the accuracy of RUL prediction. Consequently, a novel Bayesian adversarial Fast Linear Attention with a Single Head (FLASH) Transformer with feature disentanglement model (BAFTFD) was proposed in this article to tackle with the problem. The proposed BAFTFD model can disentangle the private feature representations from the raw data, preserving the shared feature representation for the prediction. The adversarial training method is also exploited to facilitate the transfer of degradation knowledge. Besides, the feature extractor is equipped with the effective FLASH Transformer model to retain the most informative degradation features for model training, improving the efficiency of feature extraction. Moreover, considering the impact of insufficient training data, inherent data noise on the trustworthiness of the predictive results, the Bayesian DL method is adopted to quantify the prediction uncertainties, ensuring the reliability of maintenance decisions. Two commercial turbofan datasets are leveraged to validate the designed model.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 4","pages":"5835-5847"},"PeriodicalIF":5.7,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate degradation modeling is a prerequisite for reliable prognostics. When historical data are scarce and operating conditions vary over time, conventional approaches struggle to balance accuracy, adaptability, and interpretability, and lack robustness to changing environments, especially when encoding the effects of operating conditions directly in the model. To overcome these limitations, this paper proposes a novel generalized degradation model based on a semi-physics-informed neural stochastic differential equation, where neural stochastic differential equation (NSDE) is utilized to describe the degradation dynamics. In contrast, the effect of operating conditions on degradation rate is injected in a plug-and-play prior without being locked into the NSDE structure. A variational inference-based generative training procedure jointly estimates the parameters of the NSDE and the prior, mitigating the adverse effect of imperfect physics and requiring only modest historical data. Then, an approximate closed-form distribution for the remaining useful lifetime (RUL) is derived. Thus, an approach for RUL prognostics of in-service products under dynamic operating conditions is established, leveraging the knowledge of degradation from historical data. Comprehensive studies on simulated and battery degradation data demonstrate the robustness and effectiveness of the proposed model.
{"title":"A Generalized Degradation Model Based on Semi-Physics-Informed Neural Stochastic Differential Equation","authors":"Zirong Wang;Zhen Chen;Tangbin Xia;Ershun Pan","doi":"10.1109/TR.2025.3620010","DOIUrl":"https://doi.org/10.1109/TR.2025.3620010","url":null,"abstract":"Accurate degradation modeling is a prerequisite for reliable prognostics. When historical data are scarce and operating conditions vary over time, conventional approaches struggle to balance accuracy, adaptability, and interpretability, and lack robustness to changing environments, especially when encoding the effects of operating conditions directly in the model. To overcome these limitations, this paper proposes a novel generalized degradation model based on a semi-physics-informed neural stochastic differential equation, where neural stochastic differential equation (NSDE) is utilized to describe the degradation dynamics. In contrast, the effect of operating conditions on degradation rate is injected in a plug-and-play prior without being locked into the NSDE structure. A variational inference-based generative training procedure jointly estimates the parameters of the NSDE and the prior, mitigating the adverse effect of imperfect physics and requiring only modest historical data. Then, an approximate closed-form distribution for the remaining useful lifetime (RUL) is derived. Thus, an approach for RUL prognostics of in-service products under dynamic operating conditions is established, leveraging the knowledge of degradation from historical data. Comprehensive studies on simulated and battery degradation data demonstrate the robustness and effectiveness of the proposed model.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 4","pages":"5820-5834"},"PeriodicalIF":5.7,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jiantai Wang;Yu Zhao;Xiaobing Ma;Hui Xiao;Yuhan Ma;Rui Peng;Li Yang
Inspection errors are extensively reported in equipment health management due to multisource noises and technical limitations, particularly in hidden defect diagnosis of the multistage failure process. This article proposes a state-age-dependent maintenance and spare control strategy to compensate inspection-error-induced risk (attributed to both false positive and false negative) during defect identification. Specifically, a dual-phase adaptive inspection accommodating health variation is scheduled, following which both spare ordering and replacement are postponed to compensate implication of false-positive error. In addition, age-based replacement supported by preponed standard ordering is implemented promptly to alleviate false-negative error impact. To mitigate downtime losses, a dynamic selection mechanism upon failure occurrence between urgent and standard orderings is executed. The long-run operational cost rate is minimized by the joint optimization of postponed intervals of ordering and replacement, as well as the second-phase inspection interval. The model applicability is demonstrated through numerical experiments conducted on high-speed train bogie bearings.
{"title":"A State-Age-Dependent Maintenance-Spare Control Strategy Under Inspection Error Compensation","authors":"Jiantai Wang;Yu Zhao;Xiaobing Ma;Hui Xiao;Yuhan Ma;Rui Peng;Li Yang","doi":"10.1109/TR.2025.3607028","DOIUrl":"https://doi.org/10.1109/TR.2025.3607028","url":null,"abstract":"Inspection errors are extensively reported in equipment health management due to multisource noises and technical limitations, particularly in hidden defect diagnosis of the multistage failure process. This article proposes a state-age-dependent maintenance and spare control strategy to compensate inspection-error-induced risk (attributed to both false positive and false negative) during defect identification. Specifically, a dual-phase adaptive inspection accommodating health variation is scheduled, following which both spare ordering and replacement are postponed to compensate implication of false-positive error. In addition, age-based replacement supported by preponed standard ordering is implemented promptly to alleviate false-negative error impact. To mitigate downtime losses, a dynamic selection mechanism upon failure occurrence between urgent and standard orderings is executed. The long-run operational cost rate is minimized by the joint optimization of postponed intervals of ordering and replacement, as well as the second-phase inspection interval. The model applicability is demonstrated through numerical experiments conducted on high-speed train bogie bearings.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 4","pages":"5805-5819"},"PeriodicalIF":5.7,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The optimization of preventive maintenance (PM) intervals traditionally follows predict-then-optimize (PTO) frameworks. These involve two sequential steps: training a statistical model to estimate the failure time distribution (FTD) and then integrating it into an optimization model for deciding the optimal PM interval. However, PTO models may have poor out-of-sample performance if the fitted FTD differs significantly from the true distribution or fails to capture covariate effects. To overcome these issues, this paper introduces a contextual distributionally robust optimization (DRO) model for computing PM intervals. The proposed model integrates empirical failure time data directly into the optimization framework without assuming specific distributions. Our setting assumes that the component FTD is affected by covariates. Therefore, our formulation seeks to exploit covariate knowledge to compute efficient PM decisions conditional on the observed covariates. We formulate a DRO model that accounts for potential misspecifications of the empirical FTD. This DRO formulation aims to minimize the long-term maintenance cost rate by optimizing PM decision policies over an infinite space, where these policies map covariate information to optimal PM intervals. We demonstrate that the proposed DRO model admits tractable mixed-integer linear programming reformulations in various practical cases. The efficacy of our model is demonstrated through computational studies involving simulated and real-world failure time data.
{"title":"On the Computation of Contextual Distributionally Robust Preventive Maintenance Intervals","authors":"Heraldo Rozas;Nagi Gebraeel;Weijun Xie","doi":"10.1109/TR.2025.3603869","DOIUrl":"https://doi.org/10.1109/TR.2025.3603869","url":null,"abstract":"The optimization of preventive maintenance (PM) intervals traditionally follows predict-then-optimize (PTO) frameworks. These involve two sequential steps: training a statistical model to estimate the failure time distribution (FTD) and then integrating it into an optimization model for deciding the optimal PM interval. However, PTO models may have poor out-of-sample performance if the fitted FTD differs significantly from the true distribution or fails to capture covariate effects. To overcome these issues, this paper introduces a contextual distributionally robust optimization (DRO) model for computing PM intervals. The proposed model integrates empirical failure time data directly into the optimization framework without assuming specific distributions. Our setting assumes that the component FTD is affected by covariates. Therefore, our formulation seeks to exploit covariate knowledge to compute efficient PM decisions conditional on the observed covariates. We formulate a DRO model that accounts for potential misspecifications of the empirical FTD. This DRO formulation aims to minimize the long-term maintenance cost rate by optimizing PM decision policies over an infinite space, where these policies map covariate information to optimal PM intervals. We demonstrate that the proposed DRO model admits tractable mixed-integer linear programming reformulations in various practical cases. The efficacy of our model is demonstrated through computational studies involving simulated and real-world failure time data.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 4","pages":"5792-5804"},"PeriodicalIF":5.7,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145652150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"IEEE Reliability Society Publication Information","authors":"","doi":"10.1109/TR.2025.3600980","DOIUrl":"https://doi.org/10.1109/TR.2025.3600980","url":null,"abstract":"","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"C2-C2"},"PeriodicalIF":5.7,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11152594","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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