Publication Type : Journal Article
Publisher : Measurement Science and Technology
Source : Measurement Science and Technology, Institute of Physics Publishing, Volume 30, Issue 8 (2019)
Url : https://www2.scopus.com/inward/record.uri?eid=2-s2.0-85069055158&doi=10.1088%2f1361-6501%2fab0737&partnerID=40&md5=6d18a3217e9f10474522b8f8952c6f89
Keywords : Acoustic emission sensors, Acoustic emission testing, Condition monitoring, Different operating conditions, Failure analysis, Fault detection, Gears, Learning algorithms, Machine learning, Maintenance, Multi-sensor fusion, Multi-sensor information fusion, Neural networks, Outages, Proximal support vector machines, PSVM, Support vector machines, vibration, Vibrations (mechanical), wavelet
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Mechanical Engineering
Year : 2019
Abstract : A gear box is widely employed in automobiles and industrial machines for transmitting power and torque. It operates under various working conditions for prolonged hours increasing the chance of gearbox failure. Major faults in gear systems are caused due to wear, scoring, pitting, tooth fracture, etc. Gear box failure leads to increases in machine downtime and maintenance costs. The nature and location of such failures can be identified with precision using condition monitoring techniques. In this study, machine condition data are acquired from the gear box using a vibration accelerometer, microphone and acoustic emission sensors under different operating conditions, such as three loading conditions (0 N, 5 N, 10 N) and three rpm variations (500, 750, 1000). The wavelet features are extracted from the acquired vibration, sound and acoustic emission signal, and prominent features are identified. To automate the process of fault diagnosis, machine learning algorithms (artificial neural network, support vector machine, and proximal support vector machine) are utilized. Dual and multi-sensor fusion is implemented with the help of prominent features, to intensify the classification accuracy. The performance of the individual signals, and dual and multi-sensor fused models in gearbox fault diagnosis are compared and discussed in detail. © 2019 IOP Publishing Ltd.
Cite this Research Publication : P. T. Kumar, Dr. Saimurugan M., Haran, R. B. Hari, Siddharth, S., and Dr. K. I. Ramachandran, “A multi-sensor information fusion for fault diagnosis of a gearbox utilizing discrete wavelet features”, Measurement Science and Technology, vol. 30, no. 8, 2019.