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- Lou, Chuyue, et al.
(author)
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Recent deep learning models for diagnosis and health monitoring : a review of researches and future challenges
- 2023
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In: Transactions of the Institute of Measurement and Control. - London : Sage Publications. - 0142-3312 .- 1477-0369.
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Journal article (peer-reviewed)abstract
- As an important branch of machine learning, deep learning (DL) models with multiple hidden layer structures have the ability to extract highly representative features from the input. At present, fault detection and diagnosis (FDD) and health monitoring solutions developed based on DL models have received extensive attention in academia and industry along with the rapid improvement of computing power. Therefore, this paper focuses on a comprehensive review of DL model–based FDD and health monitoring schemes in view of common problems of industrial systems. First, brief theoretical backgrounds of basic DL models are introduced. Then, related publications are discussed about the development of DL and graphical models in the industrial context. Afterwards, public data sets are summarized, which are associated with several research papers. More importantly, suggestions on DL model–based diagnosis and health monitoring solutions and future developments are given. Our work will have a positive impact on the selection and design of FDD solutions based on DL and graphical models in the future. © The Author(s) 2023.
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| 2. |
- Lou, Chuyue, et al.
(author)
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Unknown Health States Recognition with Collective-Decision-Based Deep Learning Networks in Predictive Maintenance Applications
- 2024
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In: Mathematics. - Basel : MDPI. - 2227-7390. ; 12:1
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Journal article (peer-reviewed)abstract
- At present, decision-making solutions developed based on deep learning (DL) models have received extensive attention in predictive maintenance (PM) applications along with the rapid improvement of computing power. Relying on the superior properties of shared weights and spatial pooling, convolutional neural networks (CNNs) can learn effective representations of health states from industrial data. Many developed CNN-based schemes, such as advanced CNNs that introduce residual learning and multi-scale learning, have shown good performance in health states recognition tasks under the assumption that all the classes are known. However, these schemes have no ability to deal with new abnormal samples that belong to state classes not part of the training set. In this paper, a collective decision framework for different CNNs is proposed. It is based on a one-vs.-rest network (OVRN) to simultaneously achieve classification of known and unknown health states. OVRNs learn class-specific discriminative features and enhance the ability to reject new abnormal samples incorporated to different CNNs. According to the validation results on the public dataset of the Tennessee Eastman process (TEP), the proposed CNN-based decision schemes incorporating an OVRN have outstanding recognition ability for samples of unknown heath states while maintaining satisfactory accuracy on known states. The results show that the new DL framework outperforms state-of-the-art CNNs, and the one based on residual and multi-scale learning has the best overall performance. © 2023 by the authors.
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