A method for fault detection in multi-component systems based on sparse autoencoder-based deep neural networks

•We consider the problem of detecting failures in multi-component system.•The autoencoder-based method is able to automatically extract degradation indicators.•A single run-to-failure trajectory is enough to pre-train the deep neural network.•The computational burden of deep neural network hyperpara...

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Bibliographic Details
Published in:Reliability engineering & system safety Vol. 220; p. 108278
Main Authors: Yang, Zhe, Baraldi, Piero, Zio, Enrico
Format: Journal Article
Language:English
Published: Barking Elsevier Ltd 01.04.2022
Elsevier BV
Elsevier
Subjects:
Artificial neural networks
Computer applications
Deep learning
Deep neural network
Engineering Sciences
Evolution
Evolving environment
Fault detection
Multi-component system
Neural networks
Reliability engineering
Sparse autoencoder
Deep learning
Deep neural network
Multi-component system
Evolving environment
Sparse autoencoder
Fault detection
ISSN:0951-8320, 1879-0836
Online Access:Get full text
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Summary:•We consider the problem of detecting failures in multi-component system.•The autoencoder-based method is able to automatically extract degradation indicators.•A single run-to-failure trajectory is enough to pre-train the deep neural network.•The computational burden of deep neural network hyperparameter setting is reduced.•The proposed method allows dealing with unbalanced dataset. 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.
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ISSN:0951-8320
1879-0836
DOI:10.1016/j.ress.2021.108278