A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM

By Xiaoyuan Zhang, Yi-tao Liang, Jian-zhong Zhou, Yi Zang. 2015

554 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/f8176b18565a99b4b676aeb7ab440882b440c6b0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Xiang Li
2. Qian Ding
3. Wei Zhang
4. Yaguo Lei
5. Jing Lin
6. Dong Yang
7. Weiting Zhang
8. Hongchao Wang

In Mechanical Engineering

1. Wei Zhang
2. Xiang Li
3. Qian Ding
4. Yaguo Lei
5. Dong Yang
6. Weiting Zhang
7. Hongchao Wang
8. Jing Lin

Citations in the corpus

• Multi-Layer domain adaptation method for rolling bearing fault diagnosis (2019, background): “In [65], [66] and [67], 10 or more bearing conditions were classified, and 88 .”
• Cross-Domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural Networks (2019, background): “In [42], [43], and [44], ten or more conditions were classified, and 88.”
• An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data (2016, methodology): “[44] proposed a bearing diagnosis method integrating permutation entropy, ensemble empirical mode decomposition, and optimized SVM.”
• Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey (2019, methodology): “A hybrid SVM model optimized by the intercluster distance in the feature space was developed in [77] to extract the multiple temporal scale features from a vibration signal and was used to identify the fault types and their severity with respect to motor bearings.”