Innovative defect detection in surface-modified gears using time–frequency domain analysis and deep learning models

Gears play a pivotal role in power transmission systems. Over prolonged periods of high-load transmission, the issue of gear lifespan has become a challenging one to address. Monitoring systems are vital in today’s smart manufacturing landscape, contributing to safety and sustainable development. Presently, there is a dearth of research focused on the vibration of gears with surface modification. Consequently, this study has devised a novel defect detection model for surface modification gears. It employs time-domain, frequency-domain, and discrete wavelet transform (DWT) analysis in combination with deep neural network (DNN) deep learning to validate the effectiveness of this approach. The study deals with gears in two states: undamaged and wear. Vibration data was sampled for 800 s at a rate of 1024 Hz. Leveraging artificial intelligence, this research opens the door to innovative applications and services in the field of gear manufacturing. Notably, DWT exhibits a significant reduction in the amplitude of vibrations between surface modification helical gears and surface modification spur gears. Furthermore, from the DNN training and accuracy, it is evident that there is no substantial difference in accuracy between epochs 30 and epochs 100.