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http://dx.doi.org/10.5050/KSNVE.2010.20.11.1009

Feature Vector Decision Method of Various Fault Signals for Neural-network-based Fault Diagnosis System  

Han, Hyung-Seob (울산대학교 대학원 컴퓨터정보통신공학과)
Cho, Sang-Jin (울산대학교 전기전자정보시스템공학부)
Chong, Ui-Pil (울산대학교 컴퓨터정보통신공학부)
Publication Information
Transactions of the Korean Society for Noise and Vibration Engineering / v.20, no.11, 2010 , pp. 1009-1017 More about this Journal
Abstract
As rotating machines play an important role in industrial applications such as aeronautical, naval and automotive industries, many researchers have developed various condition monitoring system and fault diagnosis system by applying various techniques such as signal processing and pattern recognition. Recently, fault diagnosis systems using artificial neural network have been proposed. For effective fault diagnosis, this paper used MLP(multi-layer perceptron) network which is widely used in pattern classification. Since using obtained signals without preprocessing as inputs of neural network can decrease performance of fault classification, it is very important to extract significant features of captured signals and to apply suitable features into diagnosis system according to the kinds of obtained signals. Therefore, this paper proposes the decision method of the proper feature vectors about each fault signal for neural-network-based fault diagnosis system. We applied LPC coefficients, maximum magnitudes of each spectral section in FFT and RMS(root mean square) and variance of wavelet coefficients as feature vectors and selected appropriate feature vectors as comparing error ratios of fault diagnosis for sound, vibration and current fault signals. From experiment results, LPC coefficients and maximum magnitudes of each spectral section showed 100 % diagnosis ratios for each fault and the method using wavelet coefficients had noise-robust characteristic.
Keywords
Fault Diagnosis; Feature Extraction; LPC Coefficient; Wavelet Transform; Neural Network; Rotating Machine;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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