Bearing Fault Detection and Classification: A Framework Approach

Abstract

Bearings are the major components in rotary machinery and very used in the industry. The time for bearing failures identification before interrupting operation or affecting product quality is the basis for most predictive maintenance programs. Taking readings, keeping history of failures and evaluating these results in the operation of rotating equipment on a regular basis, allows to detect possible failures before they become catastrophic. In this way, the damages or defects that are detected before a failure occurs, reduce the repair costs and the time that a rotating machine will be inactive. The bearing failures can generate losses due to machine downtime, unwanted vibration, noise and damage of other components, but if they are detected in time, repair costs and downtime are minimal. This article shows in detail the different detection and classification techniques most used to identify bearing failures such as vibration analysis, artificial neural networks (i.e ANN), convolutional neural networks (i.e CNN) and support vector machine (i.e SVM) and the relevant features of each detection technique.