Intelligent Fault Diagnosis of Bearings based on Convolutional Neural Network using Infrared Thermography

Abstract

Bearings, as a key part of rotating machinery, are prone to failure due to fatigue and aging resulting from their long-term and high-load operation. To ensure stability of the mechanical equipment, monitoring bearing health is helpful to guarantee smooth operation of machinery and increasing machinery availability. This article puts forward an intelligent non-invasive thermal images-based fault diagnostic approach to periodically monitor condition of the rolling contact bearings in respect of their deterioration due to defects on the inner race, outer race and balls/rollers. Thermal images of four bearing conditions, including one healthy and three faulty states, have been considered followed by a performance classification based comparative analysis using Support Vector Machines (SVM) and Convolutional Neural Network (CNN). The CNN consists of many tools under its cap but for this work, the AlexNet architecture is used which has proved to be more effective than SVM. The experimental findings reveal that non-contact infrared thermography has enormous potential for automatically identifying problems and detecting early warning, regardless of speed, resulting in negligible shutdowns of the system due to bearing failure.