THE APPLICATION OF SOME NON-LINEAR METHODS IN ROTATING MACHINERY FAULT DIAGNOSIS

By William Wang, J. Chen, Xin Wu, Zongxiao Wu. 2001

118 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/ae6f325c924e3cb437e9a1d936ab659fe452bfd9

How researchers see this paper

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

In Artificial Intelligence

1. Hussein Al-Bugharbee
2. I. Trendafilova
3. Ming Jian Zuo

In Control and Systems Engineering

1. Hussein Al-Bugharbee
2. I. Trendafilova
3. F. Chu
4. Ming Jian Zuo
5. Long Zhang
6. Hesheng Liu
7. Weizhong Guo
8. Huijun Zou

In Mechanics of Materials

1. Hussein Al-Bugharbee
2. I. Trendafilova
3. F. Chu
4. Zhipeng Feng

Citations in the corpus

• A fault diagnosis methodology for rolling element bearings based on advanced signal pretreatment and autoregressive modelling (2016, methodology): “Recently, it has been used for health monitoring of tool wear[42], rotor rub problems [43] and fault diagnosis in rolling element bearings [44-46].”
• Application of regularization dimension to gear damage assessment (2010, methodology): “[10] researched the application of some nonlinearity analysis methods, including the pseudo-phase portrait, singular spectrum, and correlation dimension, for monitoring and diagnosis of rotating machinery, and they found that the correlation dimension can be used to indicate the number of state vari”
• A comparative study on ApEn, SampEn and their fuzzy counterparts in a multiscale framework for feature extraction (2010, background): “Correlation dimension (CoD) is among these efforts for feature extraction (Logan and Mathew, 1996; Jiang et al., 1999; Wang et al., 2001 ).”