Intelligent Fault Diagnosis Model for Rotating Machinery Based on Fusion of Sound Signals

SAIMURUGAN MUTHUSAMY and Nithesh R
Publication Target: 
IJPHM
Publication Issue: 
2
Submission Type: 
Full Paper
AttachmentSizeTimestamp
ijphm_16_018.pdf990.02 KBNovember 19, 2016 - 1:34am

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.

Publication Year: 
2016
Publication Volume: 
7
Publication Control Number: 
018
Page Count: 
10
Submission Keywords: 
data mining
Neural Networks
data fusion
fault diagnosis
SOUND SIGNALS
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Submitted by: 
  
 
 
 

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