IEEE Transactions on Reliability最新文献

{"title":"Predictive Maintenance Edge Artificial Intelligence Application Study Using Recurrent Neural Networks for Early Aging Detection in Peristaltic Pumps","authors":"Juan M. Montes-Sánchez;Yoko Uwate;Yoshifumi Nishio;Saturnino Vicente-Díaz;Ángel Jiménez-Fernández","doi":"10.1109/TR.2024.3488963","DOIUrl":"https://doi.org/10.1109/TR.2024.3488963","url":null,"abstract":"Peristaltic pumps are widely used in many industrial applications, especially in medical devices. Their reliability depends on proper maintenance, which includes the total replacement of tubes regularly due to the aging of the materials. The proper use of predictive maintenance techniques could potentially improve the efficiency of maintenance interventions and prevent failures by having a way to determine when the tube has passed its replacement time. We recorded a dataset using six different sensors (three accelerometers, one gyroscope, one magnetometer, and one microphone) using several cassettes (three new units and three units with expired life span). The recording was done at the highest possible frequency (100–6667 Hz, different for each sensor) and then downsampled several times to obtain frequencies as low as 12 Hz. This dataset is now publicly available. We trained 939 different models, which were the result of combining all different sensors as inputs but the microphone, and four basic architectures of recurrent neural network: One or two layers of either gated recurrent unit or long short-term memory with different number of nodes per layer (from 2 to 64). Among all trained models, we selected the ten best performing networks in terms of both accuracy and complexity. All of them reached an F1 score of 0.99 or 1 with holdout cross-validation. Those models were deployed on four different edge AI devices. For all combinations of model and edge AI devices we obtained metrics of memory size (from 0.3% to 160.6% RAM, and from 0.9% to 21.3% flash), inference time (from 0.39 to 1463.91 ms), and average consumption (from 0.15 to 5.30 mA). Nine out of ten models were proven viable for deployment. We concluded that the four models based on magnetometer data were significantly better in terms of consumption and inference time. To the best of our knowledge, the use of magnetometer data is a very uncommon approach to failure detection in predictive maintenance applications, and this is probably the first time it has been used for peristaltic pump aging detection, so our results are very promising for future applications. Also, since most trained models use little resources, we have proved that our approach is perfectly compatible with running other communication and control algorithms on the same device, which is ideal for easy integration and scalability in industrial systems. Some limitations for real deployment include facing environmental factors (noise) and long-term monitoring, so we also proposed a protocol that should reduce the impact of those factors by taking measurements in a controlled way.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3730-3744"},"PeriodicalIF":5.7,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10754656","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998006","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}

{"title":"ReenSAT: Reentrancy Vulnerability Detection in Smart Contracts Using Semantic-Enhanced SAT Evaluation","authors":"Long He;Xiangfu Zhao;Yichen Wang","doi":"10.1109/TR.2024.3488814","DOIUrl":"https://doi.org/10.1109/TR.2024.3488814","url":null,"abstract":"Reentrancy, a specific vulnerability in smart contracts, frequently leads to security incidents. However, existing detection tools encounter challenges related to low precision, limited mainly by eight typical false positive (FP) types. To address these challenges, we proposed enriching the control flow to construct a constraint reentrancy control flow graph (CRCFG) at the source code level. The CRCFG includes specific control flows interacting with attackers and corresponding constraint relationships. This enhancement facilitates modeling of the reentrancy process and leverages Boolean satisfiability (SAT) solvers for vulnerability detection, thereby enhancing the precision of the detection. Specifically, first, we present the concepts of five different kinds of basic blocks to build a CRCFG. Then, we encode the CRCFG by converting it into a conjunctive normal form file. Finally, we call a SAT solver to examine all scenarios in the CRCFG and determine the presence of reentrancy vulnerabilities. Based on the above-mentioned steps, we developed a tool, ReenSAT, to detect reentrancy vulnerabilities. We conducted experiments on a verified real-world dataset. Experimental results show that ReenSAT outperforms state-of-the-art tools by an impressive <bold>34.72%</b> in precision, while effectively addressing eight typical types of false positives within these tools. In addition, when processing complex large contract datasets, ReenSAT's vulnerability detection efficiency outperforms that of most state-of-the-art tools.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 2","pages":"2708-2722"},"PeriodicalIF":5.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206120","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":"New Design of Robust Model Predictive Control for Wind Power System Using Frequency Domain Bands and Dandelion-Optimizer","authors":"Shimaa Bergies;Chun-Lien Su;Mahmoud Elsisi","doi":"10.1109/TR.2024.3488122","DOIUrl":"https://doi.org/10.1109/TR.2024.3488122","url":null,"abstract":"Wind power systems often face challenges due to uncertainties in load demands and system parameters, which can affect their stability and performance. This article addresses these challenges by introducing a design of robust model predictive control (RMPC) tailored to address system uncertainties effectively. To manage uncertainty, this article formulates new frequency domain bands derived from the Hermite–Biehler theorem, ensuring the stability of wind power system amidst varying load demands and system parameters. Furthermore, the tuning of RMPC parameters, such as prediction horizon, control horizon, sample rate, and weighting factors, are optimized using an innovative dandelion-optimizer (DO) algorithm, which incorporates frequency domain bands as inequality constraints during parameter adjustment. The efficacy of the proposed RMPC design is validated using fuzzy logic (FL) and adaptive network-based fuzzy Inference system (ANFIS), demonstrating its superiority over traditional methods. Comparative assessments with other optimization algorithms from the literature highlight the effectiveness of the DO algorithm. In addition, the integral absolute error (IAE) index for the proposed RMPC is 0.0388. This value is significantly lower than the IAE values of 0.0507 for ANFIS and 0.4555 for FL control methods. This reduction in IAE demonstrates the enhanced performance and accuracy of the proposed approach when compared to traditional control strategies. Comprehensive testing under various load demand and system parameters variations substantiates the method's robustness and superior damping performance better than other existing methods.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3718-3729"},"PeriodicalIF":5.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998220","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":"Enhancing Resilience of Interdependent Supply Chain Networks Against Delay-Time Cascading Failures With Recovery Resource Allocation","authors":"Lei Zhang;Jian Zhou;Fengxia Zhang;Peirui Qiao;Yizhong Ma","doi":"10.1109/TR.2024.3485247","DOIUrl":"https://doi.org/10.1109/TR.2024.3485247","url":null,"abstract":"The growing interdependency between physical and cyber-supply networks makes it possible for disruptions to trigger cascading failures with a mix of structure failures and function failures. There are studies that proposed recovery strategies to improve the resilience of interdependent supply chain networks (ISCNs). However, they hardly ever consider the impacts of real-world failure delay time and recovery resource allocation on ISCN resilience. In this article, a delay-time mixed cascading failure (MCF) model is first proposed to describe the disruption propagation process in ISCNs. Then, three common boundary node-based recovery strategies are implemented in ISCNs subject to MCFs, and the recovery sequence of network nodes is optimized based on efficient resource allocation. Finally, through case studies on a real-life supply chain network and three artificial networks, the effectiveness of recovery strategies is evaluated by using two resilience-based metrics from the perspectives of network function and network structure. Moreover, the impacts of important tunable parameters on ISCN resilience are examined. The experimental results demonstrate that the proposed recovery strategies are superior to traditional recovery strategies. This study provides insights for future investment decision-making toward the enhancement of ISCN resilience with limited recovery resources.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 2","pages":"2983-2997"},"PeriodicalIF":5.0,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205938","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":"Parallelizing Adaptive Reliability Analysis Through Penalizing the Learning Function","authors":"Guangchen Wang;Michael Monaghan;Mimi Zhang","doi":"10.1109/TR.2024.3483307","DOIUrl":"https://doi.org/10.1109/TR.2024.3483307","url":null,"abstract":"Structural reliability analysis is essential for evaluating system failure probabilities under uncertainties, yet it often faces computational efficiency challenges. While surrogate model-based techniques, including Kriging, are known for their high accuracy and efficiency, they typically employ a sequential learning strategy, which limits their potential for parallel computation. This article introduces the Local Penalization Adaptive Learning (LP-AL) method, which facilitates parallel adaptive reliability analysis; LP-AL introduces a penalty function that emulates the process of sequential learning strategies, thereby achieving parallelization. The method also integrates a global error-based stopping criterion and a sample pool reduction strategy to enhance efficiency. We tested LP-AL with five commonly used learning functions across various engineering scenarios. The results demonstrate that LP-AL achieves high accuracy and significantly reduces computational costs, making it a viable approach for diverse structural reliability analysis tasks.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3028-3042"},"PeriodicalIF":5.7,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10747552","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998116","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}

{"title":"Probability of Detection for Dependent Observations: The Repeated Measures Method","authors":"Christine E. Knott;Christine Schubert Kabban;Eric A. Lindgren","doi":"10.1109/TR.2024.3478802","DOIUrl":"https://doi.org/10.1109/TR.2024.3478802","url":null,"abstract":"Probability of detection (POD) calculations in structural health monitoring (SHM) applications are complicated by the dependency of measurements obtained on the same structure, among other factors. This article presents a repeated measures method to extend POD signal-response modeling to correctly describe a population of repeated measurements while estimating the variance due to dependence in the observations. In particular, equations are presented which develop an autoregressive correlation structure to model continuous observations that are correlated in time. Software implementation of these models is discussed and methodology to simulate correlated datasets is presented. The combination of these tools enables a method of POD estimation in SHM applications through the appropriate mathematical extensions of the statistical modeling.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3152-3165"},"PeriodicalIF":5.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998216","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":"BiGraphormer: A Bidirectional Graphormer on Directed Causal Graph for Fault Detection in Complex Systems","authors":"Shuwen Zheng;Jie Liu;Yunxia Chen","doi":"10.1109/TR.2024.3479323","DOIUrl":"https://doi.org/10.1109/TR.2024.3479323","url":null,"abstract":"Effective fault detection of complex systems can significantly enhance safety, availability, and maintainability. Recently, graph neural networks (GNNs), which leverage spatial structures among variables, have gained substantial attention due to advancements over previous data-driven methods. However, existing GNN-based fault detection models primarily focus on adjacent neighborhood for feature fusion, neglecting the long-range dependencies in graphs. Moreover, these models often utilize undirected correlational graphs, potentially limiting their applicability and modeling efficiency for target systems. In this work, a causal graph-based bidirectional Graphormer (BiGraphormer) is proposed for complex systems fault detection. The causal relationships among monitoring variables are mined and represented as a directed acyclic causal graph, in which nodes denote variables and directed edges indicate causal influence. Then, the dependencies including the global spatial structure, the node and edge information are encoded and fused using the proposed BiGraphormer. By incorporating both ancestral cause and descendant effect nodes along the directed causal graphs, comprehensive representations are constructed for system fault detection. To validate the effectiveness of the proposed framework, a case study concerning real monitoring data of high-speed train braking systems is conducted, with the results showing efficacy of the proposed method.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3967-3976"},"PeriodicalIF":5.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998217","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":"Dynamic Event-Triggered Model-Free Reinforcement Learning for Cooperative Control of Multiagent Systems","authors":"Ke Wang;Zhuo Tang;Chaoxu Mu","doi":"10.1109/TR.2024.3485211","DOIUrl":"https://doi.org/10.1109/TR.2024.3485211","url":null,"abstract":"In this article, a novel model-free dynamic event-triggered adaptive learning control scheme is developed for continuous-time linear multiagent systems. This control scheme is different from model-based control scheme in the sense that prior knowledge of the system's model is not required. To further reduce transmission data, an event-triggered control policy based on static event-triggered mechanism (SETM) and dynamic event-triggered mechanism (DETM) is proposed. Compared to SETM, DETM may significantly produce larger average event intervals and maintain control performance. In addition, based on off-policy integral reinforcement learning, an adaptive iteration method is proposed with convergence proof. Numerical tests on both linear and nonlinear multiagent systems are conducted to demonstrate that the proposed scheme can guarantee learning performance and larger triggering intervals. Finally, the learning control scheme is tested on the multiarea power system, which can illustrate the reliability and practicality of this method. Specifically, the load frequency control problem of the multiarea power system is studied using three control schemes, revealing that DETM can achieve a better frequency response at the lowest information transmission rate and ensure the overall quality and reliability of the power system.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3166-3179"},"PeriodicalIF":5.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997994","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":"Q-Learning-Based Resilience Assessment of Weakly Coupled Cyber-Physical Power Systems","authors":"Shuliang Wang;Xiancheng Yang;Xiaodi Huang;Jianhua Zhang;Shengyang Luan","doi":"10.1109/TR.2024.3479701","DOIUrl":"https://doi.org/10.1109/TR.2024.3479701","url":null,"abstract":"The capability of cyber-physical power system (CPPS) to recover from cascading failures caused by extreme events and restore prefailure functionality is a critical focus in resilience research. In contrast to the strongly coupled systems studied by most researchers, this article examines weakly coupled CPPS, exploring result-oriented recovery approaches to enhance system resilience. Various repair methods are compared in terms of the resilience of weakly connected CPPS across different coupling modes and probabilities of failover. Utilizing the Q-learning algorithm, an optimized sequence for network restoration is obtained to minimize the negative influence of failures on network functionality while reducing power loss. The proposed method's effectiveness and generalizability have been comprehensively verified through simulation experiments by establishing weakly coupled CPPS for the IEEE 39, IEEE 118, and IEEE 300 networks and their corresponding scale-free networks. Its rationality was verified through two recovery mechanisms: single-node recovery and multinode recovery. By comparing the proposed method with heuristic recovery methods and optimization-based recovery methods, we found that it can significantly accelerate network recovery, and improve network resilience, achieving better resilience centrality. These findings provide valuable insights for decision making in CPPS recovery work.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 2","pages":"2968-2982"},"PeriodicalIF":5.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206060","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":"DeepFusion: Smart Contract Vulnerability Detection Via Deep Learning and Data Fusion","authors":"Hanting Chu;Pengcheng Zhang;Hai Dong;Yan Xiao;Shunhui Ji","doi":"10.1109/TR.2024.3480010","DOIUrl":"https://doi.org/10.1109/TR.2024.3480010","url":null,"abstract":"Given that smart contracts execute transactions worth hundreds of millions of dollars daily, the issue of smart contract security has attracted considerable attention over the past few years. Traditional methods for detecting vulnerabilities heavily rely on manually developed rules and features, leading to the problems of low accuracy, high false positives, and poor scalability. Although deep learning-inspired approaches were designed to alleviate the problem, most of them rely on monothetic features, which may result in information incompetence during the learning process. Furthermore, the lack of available labeled vulnerability datasets is also a major limitation. To address these issues, we collect and construct a dataset of five labeled smart contract vulnerabilities, and propose <italic>DeepFusion</i>, a vulnerability detection method that fuses code representation information, including program slice information and abstraction syntax tree (AST) structured information. First, we develop automated tools to extract contract vulnerability slicing information from source code, and extract structured information from source code-converted AST. Second, code features and global structured features are fused into the data. Finally, the fused data are input into the Bidirectional Long Short-Term Memory+ Attention (BiLSTM+ATT) model for smart contract vulnerability detection. The BiLSTM model can capture long-term dependencies in both directions and is more suitable for processing serialized information generated by <italic>DeepFusion</i>, while the attention mechanism can highlight the characteristic information of vulnerabilities. We conducted experiments via collecting a real smart contract dataset. The experimental results show that our method significantly outperforms the existing methods in detecting the vulnerabilities of <italic>reentrancy</i>, <italic>timestamp dependence</i>, <italic>integer overflow and underflow</i>, <italic>Use tx.origin for authentication</i>, and <italic>Unprotected Self-destruct Instruction</i> by 6.36%, 6.42%, 16.5%, 21.29%, and 25.05%, respectively. To the best of our knowledge, the latter two vulnerabilities are the first to be detected using deep learning methods.","PeriodicalId":56305,"journal":{"name":"IEEE Transactions on Reliability","volume":"74 3","pages":"3544-3558"},"PeriodicalIF":5.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998083","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}