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http://dx.doi.org/10.5050/KSNVN.2006.16.1.027

Fault Diagnosis for Rotating Machine Using Feature Extraction and Minimum Detection Error Algorithm  

Chong, Ui-pil (울산대학교 컴퓨터정보통신공학부)
Cho, Sang-jin (울산대학교 컴퓨터정보통신공학부)
Lee, Jae-yeal (울산대학교 컴퓨터정보통신공학부)
Publication Information
Transactions of the Korean Society for Noise and Vibration Engineering / v.16, no.1, 2006 , pp. 27-33 More about this Journal
Abstract
Fault diagnosis and condition monitoring for rotating machines are important for efficiency and accident prevention. The process of fault diagnosis is to extract the feature of signals and to classify each state. Conventionally, fault diagnosis has been developed by combining signal processing techniques for spectral analysis and pattern recognition, however these methods are not able to diagnose correctly for certain rotating machines and some faulty phenomena. In this paper, we add a minimum detection error algorithm to the previous method to reduce detection error rate. Vibration signals of the induction motor are measured and divided into subband signals. Each subband signal is processed to obtain the RMS, standard deviation and the statistic data for constructing the feature extraction vectors. We make a study of the fault diagnosis system that the feature extraction vectors are applied to K-means clustering algorithm and minimum detection error algorithm.
Keywords
Fault Diagnosis; Feature Extraction; Gradient Descent; Wavelet Transform; Rotating Machine;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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