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EngineeringComputer Science

A sparse auto-encoder-based deep neural network approach for induction motor faults classification

Wenjun Sun, Siyu Shao, Rui Zhao, Ruqiang Yan, Xingwu Zhang, Xuefeng Chen

2016.7.1MEASUREMENT

DOI: 10.1016/j.measurement.2016.04.007

tlooto Summary

Compared with traditional neural network, the SAE-based DNN can achieve superior performance for feature learning and classification in the field of induction motor fault diagnosis.

Abstract

Abstract is not available.

Citation format

SUN, Wenjun, et al. A sparse auto-encoder-based deep neural network approach for induction motor faults classification. MEASUREMENT, 2016, 89: 171–178.