Search Results - "Reliability engineering & system safety"

Improved anti-noise adaptive long short-term memory neural network modeling for the robust remaining useful life prediction of lithium-ion batteries by Wang, Shunli, Fan, Yongcun, Jin, Siyu, Takyi-Aninakwa, Paul, Fernandez, Carlos

“…•An improved ANA-LSTM model is built for RUL prediction of lithium-ion batteries.•Multiple feature collaboration is conducted for internal parameter…”
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Machine learning for reliability engineering and safety applications: Review of current status and future opportunities by Xu, Zhaoyi, Saleh, Joseph Homer

“…•We provides a synthesis of the literature on ML for reliability & safety applications.•ML can provide novel, more accurate insights than traditional…”
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Fusing physics-based and deep learning models for prognostics by Arias Chao, Manuel, Kulkarni, Chetan, Goebel, Kai, Fink, Olga

“…Physics-based and data-driven models for remaining useful lifetime (RUL) prediction typically suffer from two major challenges that limit their applicability…”
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Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism by Zhang, Jiusi, Jiang, Yuchen, Wu, Shimeng, Li, Xiang, Luo, Hao, Yin, Shen

“…Prediction of remaining useful life (RUL) is of vital significance in the prognostics health management (PHM) tasks. To deal with the reverse time series and…”
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Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process by Chen, Jinglong, Jing, Hongjie, Chang, Yuanhong, Liu, Qian

“…•A general solution is presented for RUL prediction of nonlinear deterioration process.•KPCA is selected for dimensionality reduction and nonlinear feature…”
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Machine learning-based methods in structural reliability analysis: A review by Saraygord Afshari, Sajad, Enayatollahi, Fatemeh, Xu, Xiangyang, Liang, Xihui

“…•A review of the machine learning-based structural reliability analysis methods is presented.•Artificial neural networks-based structural reliability analysis…”
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Multi-scale deep intra-class transfer learning for bearing fault diagnosis by Wang, Xu, Shen, Changqing, Xia, Min, Wang, Dong, Zhu, Jun, Zhu, Zhongkui

“…•ResNet-50 is improved to learn low-level features automatically.•Multi-scale feature extractor is embedded in models to decrease information loss.•Distance…”
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A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings by Cao, Yudong, Ding, Yifei, Jia, Minping, Tian, Rushuai

“…•Causal dilated convolution block is built to learn the temporal dependencies.•A residual attention mechanism is proposed to obtain the contribution…”
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Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction by Li, Xiang, Zhang, Wei, Ding, Qian

“…•A novel deep learning architecture is proposed for prognostics using multi-scale feature extraction scheme.•Machine remaining useful life during operation can…”
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A review of definitions and measures of system resilience by Hosseini, Seyedmohsen, Barker, Kash, Ramirez-Marquez, Jose E.

“…Modeling and evaluating the resilience of systems, potentially complex and large-scale in nature, has recently raised significant interest among both…”
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Remaining useful lifetime prediction via deep domain adaptation by da Costa, Paulo Roberto de Oliveira, Akçay, Alp, Zhang, Yingqian, Kaymak, Uzay

“…•Recurrent Neural Network for domain adaptation of remaining useful life predictions.•Domains are composed of data with different fault modes and operating…”
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Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles by Han, Te, Li, Yan-Fu

“…Recent intelligent fault diagnosis technologies can effectively identify the machinery health condition, while they are learnt based on a closed-world…”
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Support vector machine in structural reliability analysis: A review by Roy, Atin, Chakraborty, Subrata

“…•SVM is excellent to handle high dimensional problems utilizing lesser training data.•No review article specifically dedicated to the applications of SVM in…”
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A dual-LSTM framework combining change point detection and remaining useful life prediction by Shi, Zunya, Chehade, Abdallah

“…•Propose a novel Dual-LSTM framework to achieve real-time high-precision RUL prediction.•Design a new health index construction function to indicate the health…”
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A review on condition-based maintenance optimization models for stochastically deteriorating system by Alaswad, Suzan, Xiang, Yisha

“…Condition-based maintenance (CBM) is a maintenance strategy that collects and assesses real-time information, and recommends maintenance decisions based on the…”
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Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing by Zhang, Yongchao, Ji, J.C., Ren, Zhaohui, Ni, Qing, Gu, Fengshou, Feng, Ke, Yu, Kun, Ge, Jian, Lei, Zihao, Liu, Zheng

“…Fault diagnosis of rolling bearings has attracted extensive attention in industrial fields, which plays a vital role in guaranteeing the reliability, safety,…”
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Incorporation of human factors into maritime accident analysis using a data-driven Bayesian network by Fan, Shiqi, Blanco-Davis, Eduardo, Yang, Zaili, Zhang, Jinfen, Yan, Xinping

“…•Analyse the primary data to estimate the appearance frequencies of risk factors resulting in maritime accidents.•Evaluate the joint impact of human factors…”
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Probabilistic framework to evaluate the resilience of engineering systems using Bayesian and dynamic Bayesian networks by Kammouh, Omar, Gardoni, Paolo, Cimellaro, Gian Paolo

“…•Static framework to quantify the resilience of any engineering system using the Bayesian networks.•Dynamic framework to quantify the resilience of any…”
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Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework by Zhou, Taotao, Han, Te, Droguett, Enrique Lopez

“…Fault diagnosis is efficient to improve the safety, reliability, and cost-effectiveness of industrial machinery. Deep learning has been extensively…”
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Random forests for global sensitivity analysis: A selective review by Antoniadis, Anestis, Lambert-Lacroix, Sophie, Poggi, Jean-Michel

“…The understanding of many physical and engineering problems involves running complex computational models. Such models take as input a high number of numerical…”
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