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 the most important basic rotating machine elements. Detection of fault from vibration signals is widely used method in condition monitoring techniques for diagnosis of machine elements. Fault diagnosis from sound signals is cost effective than vibration signals. Sound signal analysis is 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 classification accuracy results from statistical and histogram features are obtained and compared.
Highlights
Condition monitoring and diagnostic maintenance is gaining its importance in last few decades
Back propagation neural network is used with two layers: Hidden layer is used with tan-sigmoid function and an output layer
The Classification accuracy is obtained in the form of confusion matrix from Artificial Neural Network classifier
Summary
Condition monitoring and diagnostic maintenance is gaining its importance in last few decades. Many industries are checking for highly durable and reliable machines that can be efficient and productive for long time. Major causes for failure in machine elements is due to improper design, material and manufacturing defects, installation errors etc. Failure is machinery specific and can be dangerous, so there is a need for scheduled maintenance. Various condition monitoring techniques widely used are vibration signals, sound signals, acoustic emissions, infrared thermography, wear debris analysis etc. The rotating machine elements includes shaft, bearing, gears, pulleys etc. For rotating machine elements vibration signals analysis is widely used. It is important to detect and find fault in the rotating
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