FAULT DIAGNOSIS AND CLASSIFICATION OF DEEP GROOVE BALL BEARINGS USING WAVELET TRANSFORM AND ADAPTIVE NEURO-FUZZY SYSTEM

Original Research Paper Received 06 July 2015 Accepted 09 September 2015 Available Online 28 October 2015 Today, fast and accurate fault detection is one of the major concerns in industry. Although many advanced algorithms have been implemented in the past decade for this purpose, they were very complicated or did not provide the desired results. Hence, in this paper, we have proposed an emerging method for deep groove ball bearing fault diagnosis and classification. In the first step, the vibration test signals, related to the normal and faulty bearings have been used for both the drive-end and fan-end bearings of an electrical motor. After that, one dimensional Meyer wavelet transform has been employed for signal processing in the frequency domain. Hence, the unique coefficients for each kind of fault were extracted and directed to the adaptive neuro-fuzzy system for fault classification. The intelligent adaptive neuro-fuzzy system was adopted to enhance the fault classification performance due to its flexibility and ability in dealing with uncertainty and robustness to noise. This system classifies the input data to the faults in the race or the balls of each of the fan-end and the drive-end bearings with specific fault diameters. In the final part of this study, the new experimental signals were processed in order to verify the results of the proposed method. The results reveal that this method has more accuracy and better classification performance in comparison with other methods proposed in the literature.

[1]  K. R. Al-Balushi,et al.  Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection , 2003 .

[2]  Yaguo Lei,et al.  Application of an improved kurtogram method for fault diagnosis of rolling element bearings , 2011 .

[3]  Anoushiravan Farshidianfar,et al.  Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine , 2007 .

[4]  Asoke K. Nandi,et al.  Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machines , 2006 .

[5]  Ashraf Saad,et al.  Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems , 2007, Appl. Soft Comput..

[6]  Tsair-Fwu Lee,et al.  Power Transformer Fault Diagnosis Using Support Vector Machines and Artificial Neural Networks with Clonal Selection Algorithms Optimization , 2006, KES.

[7]  V. Purushotham,et al.  Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition , 2005 .

[8]  K. Loparo,et al.  Bearing fault diagnosis based on wavelet transform and fuzzy inference , 2004 .

[9]  Witold Pedrycz,et al.  Fuzzy Systems Engineering - Toward Human-Centric Computing , 2007 .

[10]  Asoke K. Nandi,et al.  Fault detection using genetic programming , 2005 .

[11]  Tong Yifei,et al.  Fault detection and diagnosis of belt weigher using improved DBSCAN and Bayesian Regularized Neural Network , 2015 .

[12]  Józef Jonak,et al.  Early fault detection in gearboxes based on support vector machines and multilayer perceptron with a continuous wavelet transform , 2015, Appl. Soft Comput..

[13]  Joseph Mathew,et al.  Multiple Band-Pass Autoregressive Demodulation for Rolling-Element Bearing Fault Diagnosis , 2001 .

[14]  Mo-Yuen Chow,et al.  Neural-network-based motor rolling bearing fault diagnosis , 2000, IEEE Trans. Ind. Electron..