Application of stack marginalised sparse denoising auto‐encoder in fault diagnosis of rolling bearing. Issue 16 (22nd October 2018)

• Record Type: Journal Article
• Authors: Zhang, Junling; Chen, Zhigang; Du, Xiaolei; Xu, Xu; Yu, Miao
• Is Part Of: Journal of engineering. Volume 2018:Issue 16(2018)
• Page Start: 1772
• Page End: 1777
• Publication Date: 2018-10-22
• Subjects: rolling bearings -- fault diagnosis -- mechanical engineering computing -- computational complexity -- corrosion -- failure analysis -- cracks -- fracture -- signal denoising; media corrosion -- erosion -- rolling bearing fault diagnosis method -- two‐layer stacking method -- SDAE -- stack marginalised sparse denoising autoencoder application -- fracturing vehicle -- sparse autoencoder -- SAE -- denoising autoencoder -- DAE -- softmax classifier -- learning training -- classification testing -- computational complexity -- pitting corrosion failure -- cracking; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2018.8267
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : When a fracturing vehicle is working, it generally needs to bear high loads, media corrosion and erosion. For this special working environment, this study proposes a rolling bearing fault diagnosis method based on stack marginalised sparse denoising auto‐encoder (SDAE). This method combines the sparse auto‐encoder (SAE) and the denoising auto‐encoder (DAE) and combines the characteristics of dimensionality reduction and robustness. The method adds marginalisation to optimise the SDAE. Finally, it uses a two‐layer stacking method. The output results of the second marginalised SDAE are used as input to the softmax classifier for learning training and classification testing. This improved method (stack SDAE) improves the denoising ability, reduces the computational complexity, solves the problems of difficult parameter adjustment and slows training convergence. The experimental tests were carried out on the failure of pitting corrosion of the outer ring of the bearing, pitting failure of the inner ring, and cracking of the rolling element. The results show that the algorithm can effectively improve the accuracy of fault diagnosis of rolling bearings, and it has greatly improved than the algorithms of SAEs and DAE.