A Vibration-Based Signal Processing Approach for The Multi-Fault Diagnostics of Gear Systems

Machines rely on vibration-based condition monitoring more than any other technology for routine maintenance and problem diagnosis. Analysis of vibration in spur gear systems and problem detection are the focus of this research. Exploration into various operating conditions and vibration studies of the gear system's configuration accomplish this. Attached to the normally operating electromotor, the gear system is progressively driven faster. Consequently, tooth loading is done alternately during operation using different combinations of faulty spur gears. With this methodology, a Fast Fourier Transformer (FFT) analyzer is employed to collect and analyze vibration data originating from the gear system. The dependent functions of vibration analysis in machine defect detection and prediction are brought to light in this work. The present investigation has effectively identified issues with the spur gear system. In addition, the Support Vector Machine (SVM) that is based on the MATLAB program is utilized in order to categorize the various signals according to the various types of faults.