 A method for fault detection in multi-component systems based on sparse autoencoder-based deep neural networksZhe Yang, Piero Baraldi and Enrico Zio Reliability Engineering and System Safety, 2022, vol. 220, issue C Abstract: In multi-component systems, degradation, maintenance, renewal and operational mode change continuously the operating conditions. The identification of the onset of abnormal conditions from signal measurements taken in such evolving environments can be quite challenging, due to the difficulty of distinguishing the real cause of the signal variations. In this work, we present a method for fault detection in evolving environments that uses a Sparse Autoencoder-based Deep Neural Network (SAE-DNN) and a novel procedure that remarkably reduces the computational burden for setting the values of the hyperparameters. The method is applied to a synthetic case study and to a bearing vibration dataset. The results show that it is able to accurately detect faults in multi-component systems, outperforming other state-of-the-art methods. Keywords: Deep learning; Sparse autoencoder; Deep neural network; Fault detection; Evolving environment; Multi-component system (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:220:y:2022:i:c:s0951832021007511 DOI: 10.1016/j.ress.2021.108278 Access Statistics for this article Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares More articles in Reliability Engineering and System Safety from Elsevier |
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