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A machine-learning-based fault diagnosis approach for intelligent condition monitoring

A machine-learning-based fault diagnosis approach for intelligent condition monitoring,10.1109/ICMLC.2010.5580753,Chih-Chung Wang,Chien-Wei Lee,Chen-S

A machine-learning-based fault diagnosis approach for intelligent condition monitoring  
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We propose a machine-learning-based fault diagnosis approach for condition monitoring on the constant-speed rotating machines via vibration signals. There are mainly five phases in our approach, i.e., vibration signal measurement, discrete-wavelet-transformation-based preprocessing, feature extraction, base-line encoding, and fuzzy neural network. The advantage of this approach can identify the condition and faults of machine without sufficient diagnosis knowledge. Experimental results have demonstrated this approach is a useful tool for condition monitoring application.
Conference: Machine Learning and Cybernetics - ICMLC , pp. 2921-2926, 2010
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