IEEE Transactions on Reliability最新文献_第7页

Generalized Functional Mixed Models for Accelerated Degradation-Based Reliability Analysis 基于加速退化的可靠性分析的广义泛函混合模型

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2024-12-24 DOI: 10.1109/TR.2024.3505077

Cesar Ruiz;Haitao Liao;Edward A. Pohl

{"title":"Generalized Functional Mixed Models for Accelerated Degradation-Based Reliability Analysis","authors":"Cesar Ruiz;Haitao Liao;Edward A. Pohl","doi":"10.1109/TR.2024.3505077","DOIUrl":"https://doi.org/10.1109/TR.2024.3505077","url":null,"abstract":"As sensing technology advances, engineers can monitor a system's physical characteristics or performance measures for reliability assessments. The evolution of such measurements as the system deteriorates can be modeled as a collection of multivariate degradation processes. The system is considered failed when any of the degradation processes reaches its predetermined threshold. In practice, degradation data are highly variable due to unobserved environmental factors, unit-specific parameters induced by underlying frailties, and physical deterioration being a function of process covariates, such as load, ambient moisture, and temperature. The later relationships, however, are often approximated through empirical transformations such as the Arrhenius model. However, as the number of degradation processes increases, model flexibility and computational cost increases in standard stochastic process models. In this article, we propose an additive functional mixed effects and Gaussian process model that isolates all sources of uncertainty and provides flexibility to incorporate physics knowledge in the reliability modeling. A comprehensive simulation study and a case study on a tuner's accelerated degradation data are presented to illustrate the capability of the proposed model and statistical methods.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"4361-4372"},"PeriodicalIF":5.7,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144996096","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}

引用次数: 0

Reliable Wind Turbine Blade Performance Monitoring System Using Aerodynamic Audio Signals and Deep Learning Approaches 基于气动音频信号和深度学习方法的可靠风力发电机叶片性能监测系统

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2024-12-20 DOI: 10.1109/TR.2024.3509442

Baheti Biekezati;Hui Zhang;Yihong Cao;Yurong Chen;Yaonan Wang

{"title":"Reliable Wind Turbine Blade Performance Monitoring System Using Aerodynamic Audio Signals and Deep Learning Approaches","authors":"Baheti Biekezati;Hui Zhang;Yihong Cao;Yurong Chen;Yaonan Wang","doi":"10.1109/TR.2024.3509442","DOIUrl":"https://doi.org/10.1109/TR.2024.3509442","url":null,"abstract":"Wind turbines have emerged as a prominent and environmentally friendly energy generation solution. However, with the widespread use of new materials, ensuring the reliability of these devices has become as a critical issue. Developing efficient and cost-effective monitoring methods for the wind turbine's blades (WTBs), the most expensive components of wind turbine, has become a focal point of research. In this article, we present a novel monitoring system for WTBs that employs a deep convolutional neural network approach based on the medical auscultatory method. The system is designed to balance economic efficiency and engineering reliability. First, we proposed a lightweight WTBs monitoring framework based on edge computing that leverages the signals from the programmable logic controller output of wind turbine to enable efficient collection of relevant aerodynamic audio signals while filtering out irrelevant data. Second, we present a set of audio enhancement algorithms that employ multiscale feature extraction, self-adaptive mask targeting, and deep neural networks to reduce noise in the audio signals generated by WTBs. Third, we introduce a new approach for compressing deep convolution neural networks that makes them suitable for resource-constrained edge computing devices and efficiently utilizes audio-generated spectrograms to diagnose faults in WTBs.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"4373-4386"},"PeriodicalIF":5.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998160","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}

引用次数: 0

2024 Index IEEE Transactions on Reliability Vol. 73 IEEE可靠性学报，第73卷

IF 5 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2024-12-20 DOI: 10.1109/TR.2024.3518272

引用次数: 0

Modeling Continuous Sensor Signals and Discrete Maintenance Events Using the Action Specific-Input Output Hidden Markov Model 用动作特定输入输出隐马尔可夫模型建模连续传感器信号和离散维护事件

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2024-12-19 DOI: 10.1109/TR.2024.3511706

Abhijeet Sandeep Bhardwaj;Yonatan Mintz;Dharmaraj Veeramani

{"title":"Modeling Continuous Sensor Signals and Discrete Maintenance Events Using the Action Specific-Input Output Hidden Markov Model","authors":"Abhijeet Sandeep Bhardwaj;Yonatan Mintz;Dharmaraj Veeramani","doi":"10.1109/TR.2024.3511706","DOIUrl":"https://doi.org/10.1109/TR.2024.3511706","url":null,"abstract":"Equipment downtime is a significant challenge for many industries. In oil extraction, downtime costs can be as high as $250 000 per day. To prevent downtime, technicians manually interact with the equipment or monitor its health using sensory signals. Sensory data indirectly ascertain equipment health, while manual actions (inspections or repairs) provide a direct and precise insight but are time-consuming and costly. Thus, efficiently leveraging sensory data and outcomes of manual actions to accurately estimate the health of their equipment while finding the critical time points to schedule repairs and minimize the overall downtime is a crucial challenge faced by industries. In this article, we present a novel joint modeling approach called the action specific-input output hidden Markov model (AS-IOHMM) that integrates real-time sensor data and discrete health state information obtained by manual actions to aid prognosis and decision making of industrial equipment. In contrast to existing models that assume nondecreasing degradation without considering maintenance actions, AS-IOHMM estimates the impact of different maintenance actions on equipment health by learning action-specific transition probability matrices. We assess the effectiveness of AS-IOHMM through a numerical case study and validate its performance using mud-pump maintenance and sensory data from an oil rig, demonstrating enhanced prognosis ability and cost reduction of 7–15% over existing methods.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3056-3070"},"PeriodicalIF":5.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997991","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}

引用次数: 0

Reliability Analysis of Cyclic Accelerated Life Test Data Using Log-Location-Scale Family of Distributions Under Censoring With Application to Solder Joint Data 基于对数位置尺度分布族的循环加速寿命试验数据可靠性分析及其在焊点数据中的应用

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2024-12-18 DOI: 10.1109/TR.2024.3509446

Wenhan Zhang;Xiaojun Zhu;Mu He;Narayanaswamy Balakrishnan

{"title":"Reliability Analysis of Cyclic Accelerated Life Test Data Using Log-Location-Scale Family of Distributions Under Censoring With Application to Solder Joint Data","authors":"Wenhan Zhang;Xiaojun Zhu;Mu He;Narayanaswamy Balakrishnan","doi":"10.1109/TR.2024.3509446","DOIUrl":"https://doi.org/10.1109/TR.2024.3509446","url":null,"abstract":"Accelerated life testing is widely employed due to the high cost involved in testing high-quality products under normal operating conditions. For products exposed to continuously fluctuating stress in the working environment, cyclic stress tests become necessary. The Coffin–Manson model is commonly used when product failure is solely attributed to temperature changes (<inline-formula><tex-math>$Delta T$</tex-math></inline-formula>). However, this assumption does not always hold in many practical situations. The Norris–Landzberg model, which considers both maximum temperature and cyclic change frequency, offers much flexibility in modeling fatigue life due to cyclic temperature fluctuations. Several studies have been conducted based on the Norris–Landzberg model. However, using the multiple linear regression method without any distributional assumption may fail to provide satisfactory inferential results. This article assumes the log-location-scale family of distributions and then shows that the weighted least-squares method based on order statistics of failure times yields the best linear unbiased estimators (BLUEs) of parameters based on complete as well as Type-II censored data. We then study some properties of these BLUEs using both theory and Monte Carlo simulations. Next, we present an illustrative example involving solder joint data to demonstrate the model and the associate inferential results developed here. Finally, the optimal design procedure is discussed.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3043-3055"},"PeriodicalIF":5.7,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998128","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}

引用次数: 0

Guardians of the Ledger: Protecting Decentralized Exchanges From State Derailment Defects 分类账的守护者：保护分散的交易所免受国家脱轨缺陷的影响

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2024-12-18 DOI: 10.1109/TR.2024.3509414

Zongwei Li;Wenkai Li;Xiaoqi Li;Yuqing Zhang

{"title":"Guardians of the Ledger: Protecting Decentralized Exchanges From State Derailment Defects","authors":"Zongwei Li;Wenkai Li;Xiaoqi Li;Yuqing Zhang","doi":"10.1109/TR.2024.3509414","DOIUrl":"https://doi.org/10.1109/TR.2024.3509414","url":null,"abstract":"The decentralized exchange (DEX) leverages smart contracts to trade digital assets for users on the blockchain. Developers usually develop several smart contracts into one project, implementing complex logic functions and multiple transaction operations. However, the interaction among these contracts poses challenges for developers analyzing the state logic. Due to the complex state logic in DEX projects, many critical state derailment defects have emerged in recent years. In this article, we conduct the first systematic study of state derailment defects in DEX. We define five categories of state derailment defects and provide detailed analyses of them. Furthermore, we propose a novel deep learning-based framework S<sc>tateGuard</small> for detecting state derailment defects in DEX smart contracts. It leverages a smart contract deconstructor to deconstruct the contract into an abstract syntax tree (AST), from which five categories of dependency features are extracted. Next, it implements a graph optimizer to process the structured data. At last, the optimized data is analyzed by graph convolutional networks to identify potential state derailment defects. We evaluated S<sc>tateGuard</small> through a dataset of 46 DEX projects containing 5671 smart contracts, and it achieved 94.25% F1-score. In addition, in a comparison experiment with state-of-the-art, S<sc>tateGuard</small> leads the F1-score by 6.29%. To further verify its practicality, we used S<sc>tateGuard</small> to audit real-world contracts and successfully authenticated multiple novel common vulnerabilities and exposures.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3629-3641"},"PeriodicalIF":5.7,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998199","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}

引用次数: 0

ERS: An Adaptive Spectral Analysis Method for Fault Diagnosis ERS：一种自适应谱分析方法

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2024-12-17 DOI: 10.1109/TR.2024.3507377

Jian Cheng;Haiyang Pan;Jinde Zheng

引用次数: 0

Reliable-RPL: A Reliability-Aware RPL Protocol Using Trust-Based Blockchain System for Internet of Things 可靠RPL：一种基于信任的区块链系统的物联网可靠感知RPL协议

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2024-12-16 DOI: 10.1109/TR.2024.3508652

Aswani Devi Aguru;Amrit Pandey;Suresh Babu Erukala;Ali Kashif Bashir;Yaodong Zhu;Rajesh Kaluri;Thippa Reddy Gadekallu

{"title":"Reliable-RPL: A Reliability-Aware RPL Protocol Using Trust-Based Blockchain System for Internet of Things","authors":"Aswani Devi Aguru;Amrit Pandey;Suresh Babu Erukala;Ali Kashif Bashir;Yaodong Zhu;Rajesh Kaluri;Thippa Reddy Gadekallu","doi":"10.1109/TR.2024.3508652","DOIUrl":"https://doi.org/10.1109/TR.2024.3508652","url":null,"abstract":"Routing protocol for low-power and lossy network (RPL) is a routing protocol for resource-constrained Internet of Things (IoT) network devices. RPL has become a widely adopted protocol for routing in low-powered device networks. However, it lacks essential security features, including end-to-end security, robust authentication, and intrusion detection capabilities. Blockchain is a decentralized and immutable digital ledger that records transactions across multiple computers. It provides privacy, transparency, security, and trust. In this work, we proposed a blockchain-based reliable RPL protocol called reliable-RPL, which uses node reliability, link reliability, and relative trust scores of RPL-enabled IoT devices. The parent selection and network topology formulation are based on the proposed reliability-aware objective function. A lightweight ECC-based scheme performs registration, identification, and authentication of RPL-enabled IoT devices. The consistent topological updates from these authenticated IoT devices are used to secure routing paths in RPL-enabled networks. Using a modified trickle algorithm, we employed a reputation-based trust system that monitors and labels malicious nodes based on their reliable activities. The novelty of the proposed framework relies on integrating Contiki-NG (as fronted for IoT network simulation) and Hyperledger Fabric (as a backend for blockchain-based device authentication and trust-based attack resilience regarding rank, replay, sinkhole, and route poisoning attacks). The experimental evaluation of reliable-RPL has demonstrated its effectiveness compared to state-of-the-art methods regarding significant performance metrics, including packet loss, routing overhead, and throughput on Hyperledger Caliper.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3499-3513"},"PeriodicalIF":5.7,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998348","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}

引用次数: 0

Are Novel Deep Learning Methods Effective for Fault Diagnosis? 新的深度学习方法对故障诊断有效吗？

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2024-12-12 DOI: 10.1109/TR.2024.3510387

Dongnian Jiang;Chenxian He;Zeyang Chen;Jinjiang Zhao

{"title":"Are Novel Deep Learning Methods Effective for Fault Diagnosis?","authors":"Dongnian Jiang;Chenxian He;Zeyang Chen;Jinjiang Zhao","doi":"10.1109/TR.2024.3510387","DOIUrl":"https://doi.org/10.1109/TR.2024.3510387","url":null,"abstract":"In recent years, deep learning has become the standard approach for fault diagnosis in mechanical equipment, with models becoming increasingly complex and large in scale. Through a thorough review and analysis of existing literature, we found that while many studies report performance improvements with the latest models, there has been no comparison of state-of-the-art (SOTA) methods. This gap is primarily due to two factors: first, the wide variation in the quality and nature of fault diagnosis data leads to significant performance fluctuations across different datasets; second, the diverse preprocessing methods employed make it challenging to compare models objectively. For instance, while deep learning has demonstrated high accuracy in bearing fault diagnosis, variations in vibration signal processing methods often skew the evaluation of model performance. To address these issues and evaluate the true performance of the latest deep learning models for fault diagnosis, this article establishes a unified data processing framework that ensures fair performance comparisons across models. Using this framework, we reproduce eight SOTA deep learning models and assess their effectiveness on three publicly available bearing datasets. Additionally, we design three benchmark models to quantify performance differences. The experimental results highlight that current deep learning-based fault diagnosis methods still face significant challenges in real-world applications. Finally, the future research directions in this domain are given.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"4170-4184"},"PeriodicalIF":5.7,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998104","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}

引用次数: 0

RUL Prediction With Cross-Domain Adaptation Based on Reproducing Kernel Hilbert Space 基于核希尔伯特空间再现的RUL跨域自适应预测

IF 5.7 2区计算机科学

IEEE Transactions on Reliability Pub Date : 2024-12-09 DOI: 10.1109/TR.2024.3488792

Qin Shu;Fode Zhang;Lijuan Shen;Hon Keung Tony Ng

{"title":"RUL Prediction With Cross-Domain Adaptation Based on Reproducing Kernel Hilbert Space","authors":"Qin Shu;Fode Zhang;Lijuan Shen;Hon Keung Tony Ng","doi":"10.1109/TR.2024.3488792","DOIUrl":"https://doi.org/10.1109/TR.2024.3488792","url":null,"abstract":"Data-driven methods for predicting remaining useful life (RUL) have received considerable attention in the field of degradation data analysis. The transfer learning (TL) method offers new possibilities for RUL tasks in various operational settings. However, in many engineering applications, challenges in TL arise mainly from the scarcity or high cost of labeled data in the target domain, coupled with incomplete degradation of RUL samples within the target domain. This article proposes an innovative model named deep cross-domain transfer learning for interpretable prediction The model effectively harnesses the advantages of domain adaptation (DA) techniques in mitigating domain distribution disparities and also uses the exceptional visualization capabilities inherent in the variational autoencoder (VAE) model. This method integrates the VAE framework with regression networks and utilizes DA techniques to align feature spaces, achieving cross-domain RUL prediction with unlabeled target domain data and cross-domain visualization of the entire degradation process. The reproducing kernel Hilbert space is considered in domain adaption to control the complexity of hypothesis space. The effectiveness of the proposed method is demonstrated by analyzing the real C-MAPSS dataset.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3871-3883"},"PeriodicalIF":5.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998289","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}

引用次数: 0