Intelligent fault diagnosis of rolling bearing and gear system under fluctuating load conditions using image processing
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DOI 10.1007/s12206-020-0903-z
Journal of Mechanical Science and Technology 34 (10) 2020 Original Article DOI 10.1007/s12206-020-0903-z Keywords: · Artificial neural network (ANN) · Fault diagnosis · Feature extraction · Semivariance · Image processing · Wave atom transform
Intelligent fault diagnosis of rolling bearing and gear system under fluctuating load conditions using image processing technique Rakesh Kumar Jha and Preety D Swami Department of Electronics & Communication Engineering, RGPV, Bhopal 462033, India
Correspondence to: Rakesh Kumar Jha [email protected]
Citation: Jha, R. K., Swami, P. D. (2020). Intelligent fault diagnosis of rolling bearing and gear system under fluctuating load conditions using image processing technique. Journal of Mechanical Science and Technology 34 (10) (2020) ?~?. http://doi.org/10.1007/s12206-020-0903-z
Received March 25th, 2020 Revised
July 16th, 2020
Accepted July 23rd, 2020 † Recommended by Editor No-cheol Park
© The Korean Society of Mechanical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract
Health monitoring of a rotating machine is mainly done by investigation of the vibration patterns generated by the machine. Leveraging the fact that faults occurring in different parts of a machine generate unique fault signatures, a fault diagnosis methodology is proposed that can identify nine different healthy and faulty categories under varying load and noisy conditions. Neural network is employed for classification of faults in various categories. The robustness of features such as semivariance, kurtosis and Shannon entropy make them strong candidates to train the artificial neural network. The matching of vibration textural patterns with wave atom basis functions ensures removal of noise. As a result, the enhanced features used to train the neural network have led to high accuracy in classification. The algorithm is tested at various load conditions for both bearing and gear fault experimental data sets acquired by machinery fault simulator in laboratory. Simulation results show high degree of accuracy for both bearing and gear fault diagnosis under no load to heavy load noisy conditions.
1. Introduction The role of machines is important not only from industrial production perspective but also in day-to-day daily life. Machines rely on bearings and gear for operations such as carrying loads and power transmission. To support the axial and radial loads, rolling bearings are employed. Gears are the mechanical devices used to transmit motion and torque between machine elements by virtue of successively grabbed teeth. Major faults in rotating machines are caused due to defects in these parts. Of the overall machinery faults, the fault contribution of bearing and gears are approximately 41 % and 20 %, respectively [1]. Improper lubrication, improper mounting, and excessive loading are the main reasons responsible for introduction of cracks and pits in rolling bearings and gears. The dimension of these flaws enlarges with advancement i
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