Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/14690
Title: Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties
Authors: Yang, Zhe
GJorgjevikj, Dejan 
Long, Jianyu
Zi, Yanyang
Zhang, Shaohui
Li, Chuan
Keywords: Deep learning, Fault diagnostics, Novelty detection, Multi-head deep neural network, Sparse autoencoder
Issue Date: 4-Jun-2021
Publisher: Springer Science and Business Media LLC
Source: Yang, Z., Gjorgjevikj, D., Long, J. et al. Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties. Chin. J. Mech. Eng. 34, 54 (2021).
Project: Macedonian - Chinese Scientific and Technological Cooperation Program Project 20-6343/1t
Journal: Chinese Journal of Mechanical Engineering
Abstract: Supervised fault diagnosis typically assumes that all the types of machinery failures are known. However, in practice unknown types of defect, i.e., novelties, may occur, whose detection is a challenging task. In this paper, a novel fault diagnostic method is developed for both diagnostics and detection of novelties. To this end, a sparse autoencoder-based multi-head Deep Neural Network (DNN) is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data. The detection of novelties is based on the reconstruction error. Moreover, the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function, instead of performing the pre-training and fine-tuning phases required for classical DNNs. The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer. The results show that its performance is satisfactory both in detection of novelties and fault diagnosis, outperforming other state-of-the-art methods. This research proposes a novel fault diagnostics method which can not only diagnose the known type of defect, but also detect unknown types of defects.
Description: Supported by National Natural Science Foundation of China (Grant Nos. 52005103, 71801046, 51775112, 51975121), Guangdong Province Basic and Applied Basic Research Foundation of China (Grant No. 2019B1515120095), Intelligent Manufacturing PHM Innovation Team Program (Grant Nos. 2018KCXTD029, TDYB2019010), MoST International Cooperation Program (6-14) and the Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje.
URI: http://hdl.handle.net/20.500.12188/14690
DOI: 10.1186/s10033-021-00569-0
Appears in Collections:Faculty of Computer Science and Engineering: Journal Articles

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