Bearing Fault Detection Based on Maximum Likelihood Estimation and Optimized ANN Using the Bees Algorithm
Publish Year: 1393
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ISME22_376
تاریخ نمایه سازی: 14 مرداد 1393
Abstract:
Rotating machinery is a common class of machinery in industry. The root cause of faults in rotating machinery is often faulty rolling element bearings. These rolling element bearings wear out easily due to the metal-metal contacts and create faults in the outer race, inner race or balls. This paper presents an algorithm using optimized artificial neural network by the Bees Algorithm for automated diagnosis of localized faults in rolling element bearings. Maximum likelihood estimation values extracted from time-domain vibration signals, real Cepstrum, minimum phase reconstruction, discrete cosine transform, discrete Fourier transform, envelope analysis signal and the Hilbert transform are used as input features for the neural network. The proposed procedure requires only a few input features, resulting in simple preprocessing and faster training. Effectiveness of the proposed method is illustrated using the experimentally obtained bearing vibration data.
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Authors
Behrooz Attaran
Master of science, Mechanical Engineering Department, Shahid Chamran University of Ahvaz
Afshin Ghanbarzadeh
Assistant Professor, Mechanical Engineering Department, Shahid Chamran University of Ahvaz