Article
Computer Science, Artificial Intelligence
Yupeng Wei, Dazhong Wu
Summary: We introduce a novel spectral graph convolutional operation and self-attention mechanism for predicting the remaining useful life (RUL) of bearings. It can handle both temporal-correlated and feature-correlated graphs, and automatically learn graph structures, leading to improved prediction accuracy and robustness.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Engineering, Electrical & Electronic
Wei Wang, Gongbo Zhou, Guoqing Ma, Xiaodong Yan, Ping Zhou, Zhenzhi He, Tianbing Ma
Summary: This article proposes a novel competitive temporal convolutional network (CTCN) for predicting the remaining useful life (RUL) of rolling bearings. The deep learning model's feature extraction is enhanced by a novel dual competitive attention (DCA) mechanism. The experimental results demonstrate that the proposed CTCN is effective and the DCA significantly improves the accuracy of RUL prediction. Additionally, the proposed global competition (GC) and multidimensional competition (MC) modules improve the performance of the attention mechanism.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Electrical & Electronic
Huachao Peng, Bin Jiang, Zehui Mao, Shangkun Liu
Summary: This article proposes a novel multiscale temporal convolutional transformer (MTCT) to simultaneously extract long-term degradation features and local contextual associations directly from raw monitoring data. The proposed method incorporates local context modeling into global modeling to capture more accurate long-term dependency coupling and alleviate the influences of stochastic noises. Experimental results on a real-world dataset of bearings demonstrate the effectiveness and superiority of the proposed method.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Electrical & Electronic
Jie Liu, Zian Yang, Jingsong Xie, Ruijie Wang, Shanhui Liu, Darun Xi
Summary: This article proposes a feature fusion-based method for predicting the remaining useful life (RUL) of rolling bearings. The method utilizes self-organizing map (SOM) and principal component analysis (PCA) for feature screening and dimensionality reduction. A bidirectional long short-term memory network combined with self-attention mechanism is used for RUL prediction. Experimental results show that the proposed method accurately predicts bearing RUL and outperforms other methods.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Information Systems
Haitao Wang, Jie Yang, Ruihua Wang, Lichen Shi
Summary: A new RUL prediction framework with convolutional attention mechanism and temporal convolutional network (CAMTCN) is proposed in this study. The vibration signals collected by multi-sensor are first normalized and directly processed as the input of the network. An Efficient Adaptive Shrinkage (EAS) model is designed to eliminate noise interference and improve the prediction accuracy of RUL. The attention mechanism is used to capture the contribution degree of bearing degradation features collected by different sensors, achieving more accurate end-to-end RUL prediction.
Article
Engineering, Mechanical
Tongyang Pan, Sui Zhang, Fudong Li, Jinglong Chen, Aimin Li
Summary: In this paper, a meta network pruning framework with attention augmented convolutions is proposed for accurate remaining useful life (RUL) prediction of liquid rocket engines. To address the problem of distribution discrepancy in engineering data under transient working conditions, a data-driven distribution matching strategy is designed. Besides, to improve the prediction accuracy and computation complexity of the model, an iterative meta network pruning algorithm is developed, which automatically calculates the meta-gradients of each convolutional kernel according to the chain rule and deletes unimportant connections in the framework. The method is verified on a high-precision cryogenic rocket engine bearing experiment platform under liquid nitrogen and outperforms benchmark algorithms.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Physics, Multidisciplinary
Lin Song, Jun Wu, Liping Wang, Guo Chen, Yile Shi, Zhigui Liu
Summary: This paper proposes a novel feature reuse multi-scale attention residual network (FRMARNet) for the remaining useful life (RUL) prediction of rolling bearings. The network automatically selects more important information using a cross-channel maximum pooling layer and extracts multi-scale degradation information through a lightweight feature reuse multi-scale attention unit. Experimental results demonstrate that the proposed FRMARNet model improves prediction accuracy while reducing the number of model parameters, outperforming other state-of-the-art methods.
Article
Engineering, Electrical & Electronic
Fei Jiang, Kang Ding, Guolin He, Huibin Lin, Zhuyun Chen, Weihua Li
Summary: This article proposes a dual-attention-based convolutional neural network for predicting the remaining useful life (RUL) of rolling bearings. The method accurately divides the prediction stages and extracts signal features to improve the effectiveness and robustness of RUL prediction.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Physics, Multidisciplinary
Yaping Wang, Jinbao Wang, Sheng Zhang, Di Xu, Jianghua Ge
Summary: This study proposes a prediction model for remaining useful life based on multiscale fusion permutation entropy (MFPE) and a multiscale convolutional attention neural network (MACNN) to address the redundant information problem in rolling bearing degradation characteristics. The model effectively learns feature information in complex time series and improves the accuracy of remaining useful life prediction compared to other models.
Article
Engineering, Electrical & Electronic
Zong Meng, Bo Xu, Lixiao Cao, Fengjie Fan, Jimeng Li
Summary: Rolling bearing is widely used in rotating machinery and plays a significant role in judging the running state and predicting the remaining useful life (RUL) of bearings. This article proposes a novel convolution network called TAFCN, which utilizes a temporal attention fusion (TAF) mechanism to describe the relationship between local and global temporal features in bearing vibration signals. The experimental results demonstrate the robustness and accuracy of TAFCN in predicting the RUL of rolling bearings.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Zijun Que, Xiaohang Jin, Zhengguo Xu
Summary: The article proposes an ensemble data-driven approach to predict the remaining useful life (RUL) of bearings, which uses feature extraction, an attention mechanism, and uncertainty analysis to improve prediction accuracy and reliability.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Computer Science, Information Systems
Yan Song, Shengyao Gao, Yibin Li, Lei Jia, Qiqiang Li, Fuzhen Pang
Summary: Collecting massive industrial data from IIoT assets improves data-driven methods for prognostics and health management systems. However, current approaches for bearing RUL prediction do not effectively weigh the contributions of data from different sensors and time steps, reducing efficiency in the big data era. A proposed deep learning-based method with an attention mechanism shows high accuracy and efficiency in practical applications.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Engineering, Industrial
Wanxiang Li, Zhiwu Shang, Maosheng Gao, Shiqi Qian, Zehua Feng
Summary: In this paper, a RUL prediction method based on a transfer multi-stage shrinkage attention temporal convolutional network under variable working conditions is proposed. The method addresses the problems of disparate distribution of degradation features and difficulties in obtaining corresponding labels by designing a shrinkage attention module, a multi-stage shrinkage attention temporal convolution block, and an unsupervised domain adaptation strategy.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Automation & Control Systems
Qiang Zhang, Zijian Ye, Siyu Shao, Tianlin Niu, Yuwei Zhao
Summary: This study proposes a novel end-to-end rolling bearing RUL prediction framework, CRAN, which integrates the advantages of CNN and LSTM and improves prediction accuracy by introducing different attention mechanisms.
ASSEMBLY AUTOMATION
(2022)
Article
Engineering, Multidisciplinary
Zhiqiang Xu, Yujie Zhang, Jianguo Miao, Qiang Miao
Summary: Aero-engines are vital for aircraft and accurately predicting their remaining useful life (RUL) is crucial for ensuring safety. This paper proposes a new global attention mechanism that combines self-attention and temporal convolutional network, resulting in an end-to-end deep learning RUL prediction method. Experimental results show the superiority of this method compared to existing ones.
Article
Computer Science, Artificial Intelligence
Xiaoli Zhao, Jianyong Yao, Wenxiang Deng, Peng Ding, Yifei Ding, Minping Jia, Zheng Liu
Summary: This paper proposes an intelligent fault diagnosis method for gearboxes under variable working conditions based on adaptive intraclass and interclass convolutional neural network (AIICNN). By applying intraclass and interclass constraints to improve sample distribution differences, and using an adaptive activation function to enlarge the heterogeneous distance and narrow the homogeneous distance of samples, the feasibility of the proposed method is verified through experimental data.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Engineering, Mechanical
Yifei Ding, Minping Jia, Qiuhua Miao, Yudong Cao
Summary: This paper introduces a novel time-frequency Transformer model to address the shortcomings of traditional models, demonstrating superior fault diagnosis performance in comparison with benchmark models and other state-of-the-art methods.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Chemical
Qiuhua Miao, Peng Huang, Yifei Ding, Jiaming Guo, Minping Jia
Summary: This paper investigates the particle mixing and segregation behavior in a rotary drum by changing the speed direction of adjacent axial segmentations. The results indicate that better mixing degree is achieved under opposite velocity direction.
Article
Computer Science, Artificial Intelligence
Peng Ding, Minping Jia, Yifei Ding, Yudong Cao, Xiaoli Zhao
Summary: This study proposes a novel meta learning algorithm, MARNN, to address the challenge of data scarcity in machinery prognostics. The experiments demonstrate the effectiveness of MARNN in obtaining desired results even with reduced on-site adaptation data.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Yifei Ding, Minping Jia, Jichao Zhuang, Peng Ding
Summary: This paper proposes a novel framework for learning imbalanced regression using cost-sensitive learning and deep feature transfer. It fills the research gap in bearing remaining useful life estimation with imbalanced data. The framework incorporates techniques such as discretization and label distribution smoothing, deep feature transfer via CORrelation ALignment (CORAL), and cost-sensitive learning via class-balanced re-weighting. The effectiveness of the framework is demonstrated through the design of various imbalanced bearing training sets and comparison with other methods.
APPLIED SOFT COMPUTING
(2022)
Article
Automation & Control Systems
Yifei Ding, Peng Ding, Xiaoli Zhao, Yudong Cao, Minping Jia
Summary: This article proposes a new framework for predicting the remaining useful life of bearings based on a multisource domain adaptation network (MDAN). By learning domain-invariant features and supervision from multiple sources, MDAN achieves better generalization performance. Case studies and comparisons with other methods validate the effectiveness of the proposed approach.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Engineering, Industrial
Yifei Ding, Minping Jia, Jichao Zhuang, Yudong Cao, Xiaoli Zhao, Chi-Guhn Lee
Summary: The success of deep learning and transfer learning has expanded the scope of fault diagnosis, especially in improving diagnosis accuracy under multiple working conditions. However, most existing approaches do not account for the diversity of fault mode distributions and weaken the generalization to imbalanced domain adaptation scenarios. This work proposes a novel deep imbalanced domain adaptation framework for fault diagnosis, which overcomes class-imbalanced label shift and improves cross-domain generalization for IDA tasks.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Computer Science, Artificial Intelligence
Yifei Ding, Minping Jia, Yudong Cao, Peng Ding, Xiaoli Zhao, Chi-Guhn Lee
Summary: This paper introduces a domain generalization (DG) approach for predicting the remaining useful life (RUL) of bearings under unseen operating conditions. It proposes an adversarial out-domain augmentation (AOA) framework to generate pseudo-domains and increase the diversity of available samples. The framework includes manifold and semantic regularization to ensure consistency. Experimental results validate the effectiveness and superiority of the proposed approach.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Automation & Control Systems
Yudong Cao, Minping Jia, Peng Ding, Xiaoli Zhao, Yifei Ding
Summary: Deep neural networks have been effective in fault classification and remaining useful life (RUL) prediction for mechanical equipment. However, traditional deep learning models are limited by network depth and require retraining for updating parameters. To address these issues, an incremental learning method based on temporal cascade broad learning system (TCBLS) is proposed. This method achieves high prediction accuracy, saves training time consumption, and handles newly acquired data without retraining.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Electrical & Electronic
Yudong Cao, Jichao Zhuang, Minping Jia, Xiaoli Zhao, Xiaoan Yan, Zheng Liu
Summary: This article proposes a complex graph neural network (CGNN-PIP) based on the picture-in-picture strategy for the remaining useful life (RUL) prediction of rotating machinery under multichannel signals. The classical graph convolution operation is upgraded to extract deep degenerate feature representations, and the picture-in-picture strategy is designed to guide graph construction. The effectiveness and superiority of the proposed method are verified through two case studies on different run-to-failure datasets. Results show that CGNN-PIP can reasonably construct the topology map of complex domain data and extract temporal and structural information reflecting equipment degradation. Comparisons with state-of-the-art methods demonstrate advantages in prediction accuracy and training consumption.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Automation & Control Systems
Yifei Ding, Peng Ding, Xiaoli Zhao, Yudong Cao, Minping Jia
Summary: This article introduces a multisource domain adaptation network (MDAN) for improving prognostics and health management of rotating machinery. MDAN effectively utilizes historical data from multiple sources, learns domain-invariant features, and achieves better generalization in the target domain.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Engineering, Industrial
Mateusz Oszczypala, Jakub Konwerski, Jaroslaw Ziolkowski, Jerzy Malachowski
Summary: This article discusses the issues related to the redundancy of k-out-of-n structures and proposes a probabilistic and simulation-based optimization method. The method was applied to real transport systems, demonstrating its effectiveness in reducing costs and improving system availability and performance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Wencheng Huang, Haoran Li, Yanhui Yin, Zhi Zhang, Anhao Xie, Yin Zhang, Guo Cheng
Summary: Inspired by the theory of degree entropy, this study proposes a new node identification approach called Adjacency Information Entropy (AIE) to identify the importance of nodes in urban rail transit networks (URTN). Through numerical and real-world case studies, it is found that AIE can effectively identify important nodes and facilitate connections among non-adjacent nodes.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hongyan Dui, Yaohui Lu, Liwei Chen
Summary: This paper discusses the four phases of the system life cycle and the different costs associated with each phase. It proposes an improvement importance method to optimize system reliability and analyzes the process of failure risk under limited resources.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Xian Zhao, Chen Wang, Siqi Wang
Summary: This paper proposes a new rebalancing strategy for balanced systems by switching standby components. Different switching rules are provided based on different balance conditions. The system reliability is derived using the finite Markov chain imbedding approach, and numerical examples and sensitivity analysis are presented for validation.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Fengyuan Jiang, Sheng Dong
Summary: Corrosion defects are the primary causes of pipeline burst failures. The traditional methodologies ignore the effects of random morphologies on failure behaviors, leading to deviations in remaining strength estimation and reliability analysis. To address this issue, an integrated methodology combining random field, non-linear finite element analysis, and Monte-Carlo Simulation was developed to describe the failure behaviors of pipelines with random defects.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Guoqing Cheng, Jiayi Shen, Fang Wang, Ling Li, Nan Yang
Summary: This paper investigates the optimal joint inspection and mission abort policies for a multi-component system with failure interaction. The proportional hazards model is used to characterize the effect of one component's deterioration on other components' hazard rates. The optimal policy is studied to minimize the expected total cost, and some structural properties of the optimal policy are obtained.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hongyan Dui, Yaohui Lu, Shaomin Wu
Summary: A new resilience model is proposed in this paper for systems under competing risks, and related indices are introduced for evaluating the system's resilience. The model takes into account the degradation process, external shocks, and maintenance interactions of the system, and its effectiveness is demonstrated through a case study.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yang Li, Jun Xu
Summary: This paper proposes a translation model based on neural network for simulating non-Gaussian stochastic processes. By converting the target non-Gaussian power spectrum to the underlying Gaussian power spectrum, non-Gaussian samples can be generated.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yanyan Liu, Keping Li, Dongyang Yan
Summary: This paper proposes a new random walk method, CBDRWR, to analyze the potential risk of railway accidents. By combining accident causation network, we assign different restart probabilities to each node and improve the transition probabilities. In the case study, the proposed method effectively quantifies the potential risk and identifies key risk sources.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Nan Hai, Daqing Gong, Zixuan Dai
Summary: The current risk management of utility tunnel operation and maintenance is of low quality and efficiency. This study proposes a theoretical model and platform that offer effective decision support and improve the safety of utility tunnel operation and maintenance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Tomoaki Nishino, Takuya Miyashita, Nobuhito Mori
Summary: A novel modeling methodology is proposed to simulate cascading disasters triggered by tsunamis considering uncertainties. The methodology focuses on tsunami-triggered oil spills and subsequent fires and quantitatively measures the fire hazard. It can help assess and improve risk reduction plans.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Mingjiang Xie, Yifei Wang, Jianli Zhao, Xianjun Pei, Tairui Zhang
Summary: This study investigates the effect of rockfall impact on the health management of pipelines with fatigue cracks and proposes a crack propagation prediction algorithm based on rockfall impact. Dynamic SIF values are obtained through finite element modeling and a method combining multilayer perceptron with Paris' law is used for accurate crack growth prediction. The method is valuable for decision making in pipeline reliability assessment and integrity management.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Saeed Jamalzadeh, Lily Mettenbrink, Kash Barker, Andres D. Gonzalez, Sridhar Radhakrishnan, Jonas Johansson, Elena Bessarabova
Summary: This study proposes an integrated epidemiological-optimization model to quantify the impacts of weaponized disinformation on transportation infrastructure and supply chains. Results show that disinformation targeted at transportation infrastructure can have wide-ranging impacts across different commodities.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Jiaxi Wang
Summary: This paper investigates the depot maintenance packet assignment and crew scheduling problem for high-speed trains. A mixed integer linear programming model is proposed, and computational experiments show the effectiveness and efficiency of the improved model compared to the baseline one.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yuxuan Tian, Xiaoshu Guan, Huabin Sun, Yuequan Bao
Summary: This paper proposes a DFMs searching algorithm based on the graph neural network (GNN) to improve computational efficiency and adaptively identify DFMs. The algorithm terminates prematurely when unable to identify new DFMs.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)