Gearbox Fault Diagnosis Using a Deep Learning Model With Limited Data Sample

Massive volumes of data are needed for deep learning (DL) models to provide accurate diagnosis results. Numerous studies of fault diagnosis systems have demonstrated the effectiveness of DL models over shallow machine learning (SL) in terms of feature extraction, feature dimensional reduction and di...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2020-10, Vol.16 (10), p.6263-6271
Main Authors: Saufi, Syahril Ramadhan, Ahmad, Zair Asrar Bin, Leong, Mohd Salman, Lim, Meng Hee
Format: Article
Language:English
Subjects:
Analytical models
Artificial neural networks
Data models
Datasets
Deep learning
Diagnostic systems
Fault diagnosis
Feature extraction
gearbox
Gearboxes
image recognition
limited data sample
Machine learning
Neural networks
Object recognition
Optimization
Pattern recognition
stacked sparse autoencoder (SSAE)
System effectiveness
Training
Wind turbines
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Summary:Massive volumes of data are needed for deep learning (DL) models to provide accurate diagnosis results. Numerous studies of fault diagnosis systems have demonstrated the effectiveness of DL models over shallow machine learning (SL) in terms of feature extraction, feature dimensional reduction and diagnosis performance. Occasionally, during data acquisition, a problem with a sensor renders some of the data potentially unsuitable for further analysis, leaving only a small data sample. To compensate for this deficiency, a DL model based on a stacked sparse autoencoder (SSAE) model is designed to deal with limited sample data. In this article, the fault diagnosis system is developed based on time-frequency image pattern recognition. Therefore, two gearbox datasets are used to evaluate the proposed diagnosis system. The results from the experiments prove that the proposed system is capable of achieving high diagnostic accuracy even with limited sample data. The proposed fault diagnosis system achieved 100% and 99% diagnosis performance on experimental gearbox and wind turbine gearbox datasets, respectively. The proposed diagnosis system increased diagnosis performance between 10% and 20% over the standard SSAE model. In addition, the proposed model achieved higher diagnosis performance compared to deep neural network and convolutional neural networks models.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2020.2967822