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

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VAGHEI YASAMAN; FARSHIDIANFAR ANOOSHIRAVAN

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Journal: Modares Mechanical Engineering Year:2016 | Volume:15 | Issue:11 Page(s): 233-241

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Title

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

Author(s)

VAGHEI YASAMAN | FARSHIDIANFAR ANOOSHIRAVAN | Issue Writer Certificate

Keywords

FAULT DIAGNOSISQ2

VIBRATION SIGNALQ2

WAVELET TRANSFORMQ2

ADAPTIVE NEURO-FUZZY SYSTEMQ2

Abstract

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.

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APA: Copy

VAGHEI, YASAMAN, & FARSHIDIANFAR, ANOOSHIRAVAN. (2016). FAULT DIAGNOSIS AND CLASSIFICATION OF DEEP GROOVE BALL BEARINGS USING WAVELET TRANSFORM AND ADAPTIVE NEURO-FUZZY SYSTEM. MODARES MECHANICAL ENGINEERING, 15(11), 233-241. SID. https://sid.ir/paper/177761/en

Vancouver: Copy

VAGHEI YASAMAN, FARSHIDIANFAR ANOOSHIRAVAN. FAULT DIAGNOSIS AND CLASSIFICATION OF DEEP GROOVE BALL BEARINGS USING WAVELET TRANSFORM AND ADAPTIVE NEURO-FUZZY SYSTEM. MODARES MECHANICAL ENGINEERING[Internet]. 2016;15(11):233-241. Available from: https://sid.ir/paper/177761/en

IEEE: Copy

YASAMAN VAGHEI, and ANOOSHIRAVAN FARSHIDIANFAR, “FAULT DIAGNOSIS AND CLASSIFICATION OF DEEP GROOVE BALL BEARINGS USING WAVELET TRANSFORM AND ADAPTIVE NEURO-FUZZY SYSTEM,” MODARES MECHANICAL ENGINEERING, vol. 15, no. 11, pp. 233–241, 2016, [Online]. Available: https://sid.ir/paper/177761/en

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