Dominant Feature Identification for Industrial Fault Detection and Isolation Applications

Author:

Zhou Jun-Hong,Pang Chee Khiang,Lewis Frank L.,Zhong Zhao-Wei

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference28 articles.

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3. Cohen, I., Tian, Q., Zhou, X.S., & Huang, T.S. (2002). Feature selection using principal feature analysis. In Proc. of the 15th International Conference on Information Processing, Rochester, NY, USA, September 22–25.

4. Dang, P., Ham, F.M., Lewis, F.L., & Stephanou, H. (2009). A Two-Stage Neural Network for Expression Classification, submitted to Neurocomputing, April.

5. Vibration: fundamentals and practice;De Silva,2006

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1. Hybrid fast unsupervised feature selection for high-dimensional data;Expert Systems with Applications;2019-06

2. An evolving approach to unsupervised and Real-Time fault detection in industrial processes;Expert Systems with Applications;2016-11

3. Technique for optimal placement of transducers for fault detection in rotating machines;Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability;2013-02-14

4. Introduction to Condition Monitoring in Mechanical and Electrical Systems;Condition Monitoring Using Computational Intelligence Methods;2011-12-17

5. References;Intelligent Diagnosis and Prognosis of Industrial Networked Systems;2011-06-22

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