A new fault diagnosis method based on deep belief network and support vector machine with Teager–Kaiser energy operator for bearings
Author:
Affiliation:
1. School of Vehicle and Energy, Yanshan University, Qinhuangdao, P.R. China
2. School of Electrical Engineering, Yanshan University, Qinhuangdao, P.R. China
Publisher
SAGE Publications
Subject
Mechanical Engineering
Link
http://journals.sagepub.com/doi/pdf/10.1177/1687814017743113
Reference20 articles.
1. On-line chatter detection and identification based on wavelet and support vector machine
2. Multiclass fault diagnosis in gears using support vector machine algorithms based on frequency domain data
3. Multi-fault diagnosis study on roller bearing based on multi-kernel support vector machine with chaotic particle swarm optimization
4. Fine-tuning Deep Belief Networks using Harmony Search
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