Research on Optimization Algorithm of auto-encoding neural network applied to rolling bearing fault diagnosis

• Published in: Journal of physics. Conference series Vol. 1871; no. 1; p. 12078
• Main Authors: Shi, Shi, Tong, Qingbin
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.04.2021
• Subjects: Algorithms; Batch processing; Fault diagnosis; Iterative methods; Machine learning; Neural networks; Optimization; Optimization algorithms; Physics; Roller bearings; Run time (computers); Signal processing
• ISSN: 1742-6588; 1742-6596; 1742-6596
• DOI: 10.1088/1742-6596/1871/1/012078

Real time, fast and batch processing of vibration signals has become a future development trend in the field of fault diagnosis, but data dimensionality disasters may arise. In view of the long running time of deep learning in the case of large samples, the gradient descent method and its variant algorithms are introduced for the loss function optimization problem, and the approximate optimal solution is solved in an iterative manner. The gradient descent method is used to minimize the loss function, and the research is carried out on the basis of MATLAB program implementation. The gradient descent method and its variant algorithm are applied to the rolling bearing fault diagnosis model for analysis. By comparing the algorithm’s convergence speed, loss value and accuracy of the rolling bearing fault diagnosis model, a relatively good optimization algorithm suitable for the rolling bearing fault diagnosis model is determined.