Bearing Fault Diagnosis Using Deep Sparse Autoencoder

• Published in: IOP conference series. Materials Science and Engineering Vol. 1062; no. 1; pp. 12002 - 12011
• Main Authors: Saufi, S R, Ahmad, Z A B, Leong, M S, Hee, L M
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.02.2021
• Subjects: ant lion optimization; Artificial neural networks; bearing; Comparative studies; Deep learning; deep sparse autoencoder; Fault diagnosis; Feature extraction; Feature recognition; Learning theory; Machine learning; Maintenance costs; Model testing; Neural networks; Optimization; Roller bearings; Support vector machines; teager kaiser energy operator
• ISSN: 1757-8981; 1757-899X
• DOI: 10.1088/1757-899X/1062/1/012002

Rolling element bearing is an important component in various machinery. Faulty on bearing cause severe equipment damage that lead to high maintenance cost. The development of deep learning has been paid a considerable amount of attention to fault diagnosis on rolling element bearing. Traditional machine learning such as Artificial Neural Network and Support Vector Machine have problems of lacking expression capacity, existing the curse of dimensionality, require manual feature extraction and require an additional feature selection. Deep learning model has the ability to effectively mine the high dimensional features and accurately recognize the health condition. In consequence, deep learning model has turned into an innovative and promising research in bearing fault diagnosis field. Thus, this paper tends to proposed Deep Sparse Autoencoder (DSAE) with Teager Kaiser Energy Operator (TKEO) to diagnose the bearing condition. DSAE is one of deep learning model which uses the architecture of neural network. During the analysis, the hyperparameter of DSAE model was optimized by Ant Lion Optimization. The analysis results show that the proposed TKEO-DSAE achieved 99.5% accuracy of the fault diagnosis. The comparative study between proposed model and ANN proved that deep learning model outperform traditional machine learning model on bearing fault diagnosis.