Development of Non-Conventional Method of Lissajous pattern for gear fault diagnosis using Machine Learning Technique

Abstract

Gears are major elements in machine parts and automotive systems, to transmitting torque and energy between components. As gears are under severe loads and stresses, they can create different types of faults and defects over period, impacting their own performance and reliability. Fault detection is an essential part of gear maintenance because that allows potential issues to be detected before they induce failures or expensive breakdowns. Technicians can identify early indications of wear, imbalance, damage, or even other concerns that might compromise their performance or lead to premature failure by monitoring the condition of gears. The industry has conventionally used vibration signals, thermography, oil analysis, and visually inspecting to diagnose faults. These techniques also have significant limitations which can decrease their accuracy in detecting faults and the cost is also very high. To overcome these limitations, new methods such as Lissajous pattern developed for fault detection in gears. These techniques have proved impressive outcomes in fault detection which have been previously undetectable using Conventional methods. This study evaluates the accuracy, performance, and cost-effectiveness of conventional and non-conventional techniques to determine the best method for fault diagnosis. Once compared to the conventional method, the Lissajous method obtained better accuracy than conventional method.