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http://dx.doi.org/10.5050/KSNVE.2011.21.11.1020

Neural-network-based Fault Detection and Diagnosis Method Using EIV(errors-in variables)  

Han, Hyung-Seob (울산대학교 전기공학부)
Cho, Sang-Jin (울산대학교 전기공학부)
Chong, Ui-Pil (울산대학교 전기공학부)
Publication Information
Transactions of the Korean Society for Noise and Vibration Engineering / v.21, no.11, 2011 , pp. 1020-1028 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 artificial neural network. 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 a neural-network-based fault diagnosis system using AR coefficients as feature vectors by LPC(linear predictive coding) and EIV(errors-in variables) analysis. We extracted feature vectors from sound, vibration and current faulty signals and evaluated the suitability of feature vectors depending on the classification results and training error rates by changing AR order and adding noise. From experimental results, we conclude that classification results using feature vectors by EIV analysis indicate more than 90 % stably for less than 10 orders and noise effect comparing to LPC.
Keywords
Fault Diagnosis; Feature Extractions; LPC Coefficients; EIV; Neural Network; Rotating Machines;
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Times Cited By KSCI : 3  (Citation Analysis)
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