Abstract
This paper proposes the expert system for accurate fault detection of bearing. The study is based upon advanced signal processing method as wavelet transform and artificial intelligence technique as artificial neural network (ANN) and K-nearest neighbor (KNN), for fault classification of bearing. An adaptive algorithm based on wavelet transform is used to extract the fault classifying features of the bearing from time domain signal. These features have been used as inputs to proposed ANN models and the same features have also been used for KNN. Dedicated experimental setup was used to perform the test upon the bearing. Single data set for four fault conditions of bearing is collected to train ANN and KNN. The processed and normalized data was trained by using backpropagation multilayer perceptron neural network. The results obtained from ANN are compared with KNN, ANN results proved to be highly effective for classification of multiple faults.
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Recommended by Associate Editor Gyuhae Park
Rohit S Gunerkar has done his B.E. (Mechanical Engineering), M.Tech. (Manufacturing Engineering) and pursuing Ph.D. (Vibration). His research areas are Vibration, Fault diagnosis, Condition Monitoring and non-linear dynamics and control.
Arun Kumar Jalan has done his B.E.(Mechanical Engineering), M.Tech. (Mechanical Engineering) and Ph.D. (Fault diagnosis). His research areas are vibration and machinery fault diagnosis. He has published various refereed journals in the field of Machinery fault diagnosis.
Sachin U Belgamwar has done his B.E. (Mechanical Engineering), M.E. (Mechanical Engineering) and Ph.D. [Mechanical Engineering (Nanocomposite)]. His research areas are composites and nano-composites. He has published an Indian patent and published papers in peer reviewed international journals.
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Gunerkar, R.S., Jalan, A.K. & Belgamwar, S.U. Fault diagnosis of rolling element bearing based on artificial neural network. J Mech Sci Technol 33, 505–511 (2019). https://doi.org/10.1007/s12206-019-0103-x
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DOI: https://doi.org/10.1007/s12206-019-0103-x