Intelligent Fault Detection for Rolling Element Bearing Based on FCKT and Deep Auto-coding Neural Network

doi:10.3901/JME.2019.07.065

Abstract

Abstract: Real-time, fast, and batch processing of vibration signals have become a future development trend in the field of fault diagnosis. However, it may lead to data dimensional disasters. In view of the fact that the long running time and the low fault identification accuracy of deep learning algorithm under the condition of large sample. The frequency spectrum correlation Kurtosis of original time domain signal is calculated under different iteration periods (FCKT) as new samples data and use the deep auto-coding neural network is used to realize the intelligent fault classification of planetary gearbox. Compared with the original samples, the new samples reduce the data dimension and shorten the analysis time. At the same time, the differences between the samples are more prominent based on the original information of each sample data. In addition, the method solves the problems that the weight parameters between layers of deep learning algorithm are set according to experience and the classification accuracy is reduced by reducing the number of hidden nodes layer by layer to improve the calculation efficiency. Finally, the validity of the algorithm is verified by comparison of experimental data.

Key words: correlation Kurtosis for different iteration periods (FCKT), deep auto-coding, intelligent fault classification

CLC Number:

TH113

YANG Rui, LI Hongkun, WANG Chaoge, HAO Baitian. Intelligent Fault Detection for Rolling Element Bearing Based on FCKT and Deep Auto-coding Neural Network[J]. Journal of Mechanical Engineering, 2019, 55(7): 65-72.

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