A selective deep stacked denoising autoencoders ensemble with negative correlation learning for gearbox fault diagnosis

SSDAE-NCL-based gearbox fault diagnosis. [Display omitted] •A novel selective DNN ensemble is proposed for gearbox fault diagnosis.•A selective SDAE with NCL (SSDAE-NCL) is developed as a recognizer.•SSDAE-NCL is easy to use for users than single DNN for gearbox fault diagnosis.•The results illustra...

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Veröffentlicht in:	Computers in industry Jg. 108; S. 62 - 72
1. Verfasser:	Yu, Jianbo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.06.2019
Schlagworte:	Deep learning Gearbox fault diagnosis Negative correlation learning Selective ensemble learning Stacked denoising autoencoder Deep learning Selective ensemble learning Stacked denoising autoencoder Gearbox fault diagnosis Negative correlation learning
ISSN:	0166-3615, 1872-6194
Online-Zugang:	Volltext
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Zusammenfassung:	SSDAE-NCL-based gearbox fault diagnosis. [Display omitted] •A novel selective DNN ensemble is proposed for gearbox fault diagnosis.•A selective SDAE with NCL (SSDAE-NCL) is developed as a recognizer.•SSDAE-NCL is easy to use for users than single DNN for gearbox fault diagnosis.•The results illustrate effectiveness of the SSDAE-NCL-based fault diagnosis method. Vibration signals are widely used as an effective way to fulfill gearbox fault diagnosis. However, it is quite challenging to extract effective fault features from noisy vibration signals and then to construct a reliable fault diagnosis model. This paper proposes a selective stacked denoising autoencoders (SDAE) with negative correlation learning (NCL) (SSDAE-NCL) for gearbox fault diagnosis. The component SDAEs are firstly constructed to extract effective fault features from vibration signals in the unsupervised-learning phase of SSDAE-NCL. Based on the extracted features, NCL is used to fine-tune the SDAE components to construct component classifiers in the supervised-learning phase of SSDAE-NCL. Finally, a selective ensemble is finished based on these divers and accurate component SDAEs for gearbox fault diagnosis. The motivation for developing ensemble of deep neural networks (DNNs) is that they can achieve higher accuracy and applicability than single component in machinery fault diagnosis. Furthermore, it can make an overall ensemble model easy to use in real cases for users, because it does not need too much prior knowledge about setup of a DNN model. The effectiveness of this SSDAE-NCL-based fault diagnosis method has been verified by experimental results on the vibration signal data from a gearbox test rig. The results illustrate that SSDAE-NCL learns effective discriminative features from vibration signals and achieves the better diagnosis accuracy in comparison with those typical DNNs (e.g., SDAE, deep belief network (DBN)).
ISSN:	0166-3615 1872-6194
DOI:	10.1016/j.compind.2019.02.015