Experimental Setup for Gear Fault Diagnosis using Machine Learning

Abstract

–There are various types of defects occur in the gear such as scoring, face wear, tooth breakage, pitting, root crack etc. Early detection of such defects is very crucial to prevent the system from sudden breakdown. Fault diagnosis is a process used for preventing breakdown. For such a fault diagnosis. we are analysing vibrations of gears. Scope of this work is to take the vibration signals for various faulty gears along with healthy gears. To classify the defects in spur gear first we need dataset for study. This paper provides the information of experimental setup to generate the dataset of vibration signals of faulty gears and healthy gear. In addition, this paper provides the details of fault generation in spur gear. Key Words: Spur Gear, Fault Diagnosis, Dataset, Vibration Signal, Tooth Breakage, Face Wear, Machine Learning.

Methods of detection and localization of the sources of noise and vibration on car gearboxes: a review

“…This wear can lead to improper gear meshing, resulting in noise, vibrations, and inefficiency in power transmission. The most common gear defects in automotive gearboxes include splines, face wear, tooth breakage, pitting, and root cracks [41]. Surface fatigue, specifically notch, and pitting defects, are common failure modes of gears [42], as shown in Fig.…”