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Article
Engineering, Industrial
Yongjun Pan et al.
Summary: In this study, various machine learning methods were comprehensively investigated for estimating the suspension parameters. The vehicle states calculated using a semi-recursive multibody model were used as inputs for training, and the results showed that the machine learning methods were able to accurately estimate the stiffness and damping coefficients of the suspension in real-time.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Industrial
Jiaxian Chen et al.
Summary: In this study, an intelligent RUL prediction approach is proposed for aero-engine by integrating multimodal data fusion methodology and ensemble transfer learning technology. The approach dynamically selects sensing data and makes robust RUL prediction under cross-working conditions. Comparative experiments on the N-CMAPSS dataset released in 2021 demonstrate that the proposed method outperforms other state-of-the-art RUL prediction methods.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Industrial
Jiusi Zhang et al.
Summary: Most supervised learning-based approaches assume that offline data and online data should have a similar distribution, which is difficult to satisfy in realistic remaining useful life prediction. To overcome this issue, a new transfer learning method called domain adaptation learning-oriented transfer learning (TL) is proposed. The method, called VLSTM-LWSAN, uses a local weighted deep sub-domain adaptation network to align fine-grained features between different degenerate stages, reducing the discrepancy between the target and source domains. Experimental results on an aircraft turbofan engine dataset demonstrate that VLSTM-LWSAN outperforms deep learning approaches without transfer learning and conventional transfer learning methods.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Review
Engineering, Industrial
Yang Hu et al.
Summary: This paper reviews the state-of-the-art of PHM from the perspectives of Design, Development, and Decision (DE3), extracting research conclusions from 235 related publications and identifying gaps and challenges in existing PHM concerning DE3. It aims to provide clear directions for advancing PHM methodologies and maturing them into practical technologies.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Engineering, Industrial
Tongyang Pan et al.
Summary: This paper proposes a multi-head attention network coupled with adaptive meta-transfer learning for accurate prediction of the remaining useful life (RUL) of cryogenic bearings in rocket engines under the steady stage. The proposed method is compared with typical benchmark algorithms using real monitoring data, and the results indicate better performance in multiple evaluation indexes.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Engineering, Industrial
Jun Xia et al.
Summary: This paper investigates the estimation of the remaining useful life (RUL) of aeroengines and proposes a novel deep learning architecture called DSAN. The effectiveness of the DSAN method is validated through experiments, showing its superiority in RUL estimation.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Engineering, Electrical & Electronic
Shahin Siahpour et al.
Summary: Thanks to the successful implementation of intelligent data-driven approaches, predicting the remaining useful life (RUL) problems has gained significant attention. Transfer learning approaches are used to transfer knowledge from source domain data to target domain data. However, there is a discrepancy between the data distribution of source and target domain datasets, which is solved by domain adaptation techniques. This article proposes a transfer learning approach for RUL prediction using consistency-based regularization to handle missing information in the incomplete target domain dataset.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Multidisciplinary
Han Cheng et al.
Summary: This study proposes a transferable convolutional neural network (TCNN) to accurately predict the remaining useful life (RUL) of bearings under various failure behaviors. By integrating multiple-kernel maximum mean discrepancies into the optimization objective, it reduces distribution discrepancies and improves the performance of the prediction model.
Article
Computer Science, Artificial Intelligence
Yongchun Zhu et al.
Summary: This study introduces a deep subdomain adaptation network (DSAN) that aligns relevant subdomain distributions across different domains based on the local maximum mean discrepancy (LMMD). DSAN is simple but effective, does not require adversarial training, and converges quickly. It can be easily integrated into feedforward network models to achieve efficient adaptation via backpropagation.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Article
Automation & Control Systems
Mohamed Ragab et al.
Summary: This article introduces a novel contrastive adversarial domain adaptation (CADA) method for cross-domain RUL prediction, which combines adversarial domain adaptation architecture with contrastive loss to effectively consider target-specific information.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Engineering, Industrial
Wei Zhang et al.
Summary: This paper proposes a transfer learning method for remaining useful life predictions using deep representation regularization. By aligning the life-cycle data of different machine entities across different operating conditions, prognostic knowledge transfer is achieved.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2021)
Article
Engineering, Electrical & Electronic
Yiwei Cheng et al.
Summary: In this article, a novel ensemble long short-term memory neural network (ELSTMNN) model is proposed for RUL prediction using a Bayesian inference algorithm to integrate multiple predictions of LSTMNNs. The effectiveness and competitive performance of the ELSTMNN-based RUL prognosis method are validated on two different turbofan engine data sets.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Engineering, Industrial
Paulo Roberto de Oliveira da Costa et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2020)
Article
Engineering, Electrical & Electronic
Wentao Mao et al.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2020)
Article
Engineering, Mechanical
Jun Zhu et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2020)
Article
Automation & Control Systems
Jinyang Jiao et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2020)
Article
Automation & Control Systems
Chuang Sun et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2019)
Article
Engineering, Mechanical
Bin Yang et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2019)
Article
Computer Science, Artificial Intelligence
Sinno Jialin Pan et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS
(2011)
