Publication

Abstract : The failure of rotating machine elements causes unnecessary downtime of the machine. Fault in the rotating machinery can be identified from noises, vibration signals obtained from sensors. Bearing and shaft are most important basic rotating machine elements. Detection of fault from vibration signals is widely used method in condition monitoring techniques. Fault diagnosis from sound signals is cost effective than vibration signals.Sound signal analysis was not well explored in the field of automated fault diagnosis. Under various simulated fault conditions, the sound signals are obtained by placing microphone near the bearing for different speeds. The features are extracted by using statistical and histogram methods. The best features of sound signals are obtained by decision tree algorithm. The extracted features are used as inputs to the classifier-Artificial Neural Network. The efficiency results from statistical and histogram features are obtained and compared.