Bearing Fault Severity Analysis on A Multi-stage Gearbox Subjected to Fluctuating Speeds

• Published in: Experimental techniques (Westport, Conn.) Conn.), 2020-10, Vol.44 (5), p.541-552
• Main Authors: Inturi, Vamsi, G.R., Sabareesh, Penumakala, P. K.
• Format: Article
• Language: English
• Subjects: Algorithms; Characterization and Evaluation of Materials; Chemistry and Materials Science; Condition monitoring; Decision trees; Defects; Diagnostic systems; Discrete Wavelet Transform; Fault diagnosis; Feature extraction; Gearboxes; Materials Science; Research Paper; Signal analysis; Support vector machines; Vibration analysis; Vibration monitoring; Wavelet transforms
• ISSN: 0732-8818; 1747-1567
• DOI: 10.1007/s40799-020-00370-z

Early detection of bearing defects may prevent the occurrence of catastrophic failures of the whole associated system. Condition monitoring strategies such as vibration and acoustic signal analyses are employed for incipient fault diagnosis of bearings. The current investigation attempts to compare the fault diagnostic capabilities in terms of their effectiveness in early detection of local bearing defects. Experiments are performed on a three-stage gearbox under constant and fluctuating operating conditions of speed. Wavelet coefficients are achieved from the acquired raw signals by discrete wavelet transform and various statistical features are obtained. Most contributing features among them are chosen by decision tree. Further, the extracted features are classified based on their fault severity levels using support vector machine algorithm. The experimental investigation revealed that vibration signal analysis outperformed the acoustic signal analysis under the experimental operating conditions.