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Unknown Health States Recognition with Collective-Decision-Based Deep Learning Networks in Predictive Maintenance Applications
Zhejiang University of Finance and Economics, Hangzhou, China.
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).
2024 (English)In: Mathematics, E-ISSN 2227-7390, Vol. 12, no 1, article id 89Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Basel: MDPI, 2024. Vol. 12, no 1, article id 89
Keywords [en]
classification, convolutional neural network, one-vs.-rest network, predictive maintenance, unknown heath states
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:hh:diva-52487DOI: 10.3390/math12010089Scopus ID: 2-s2.0-85182197617OAI: oai:DiVA.org:hh-52487DiVA, id: diva2:1831792
Available from: 2024-01-26 Created: 2024-01-26 Last updated: 2024-01-26Bibliographically approved

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Atoui, M. Amine

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