Intelligent Fault Detection of Rolling Element Bearing under Variable Operating Conditions by Convolutional Neural Network using Time and Frequency Domain Signals

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Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
Intelligent detection of rolling element bearing faults is a critical aspect of rotating equipment condition monitoring. Early detection of faults holds significant economic value for industrial units in terms of maintenance and planning. Traditional intelligent fault detection algorithms, which rely on a combination of feature extraction and signal classification, are time-consuming and require a high level of expertise. In comparison to traditional methods, Convolutional Neural Networks (CNNs) can process a large volume of data with high accuracy and automatically extract features from vibration signals. Therefore, in this research, an attempt has been made to use a simple and shallow CNN to not only determine the health state of rolling element bearings but also identify the defective element. For this purpose, a CNN model has been employed to investigate three common faults in rolling element bearings. In order to achieve the best performance, various inputs, including time waveforms, spectra, and envelopes, have been utilized. To implement and validate the algorithms, a laboratory setup was designed and constructed. After creating artificial faults on the bearings, experiments were conducted under 36 different operating conditions, comprising 9 different speeds, each at 4 different loads, encompassing four healthy states, including healthy, inner race fault, outer race fault, and rolling element fault. The obtained results have illustrated that the fault detector model with the frequency spectrum input is more accurate, with an accuracy of 95% than the models receiving the other two inputs.
Language:
Persian
Published:
Mechanical Engineering Sharif, Volume:40 Issue: 2, 2024
Pages:
38 to 51
https://www.magiran.com/p2822543