Application of statistical moments to bearing failure detection

Abstract

Failure of rolling element bearings plays a significant role in the breakdown of industrial machinery. However, the analysis of signals resulting from measurements taken from outer casings of equipment has proven to be an effective and powerful tool for the early detection of failure in bearings. Although a number of techniques have been developed over the years directed at warning of impending failure, in most cases these methods are only effective in the later stages of damage development. This paper looks at variations of the statistical moment analysis method that show potential for damage detection at a much earlier stage. This approach has several advantages over other methods in that measurements taken are essentially independent of load and speed. Data for the analysis is relatively easy to collect using an accelerometer mounted near the bearing of interest and can then be processed on a micro-computer using suitable software. An important part of the processing is separating out unwanted data from other energy sources within the machine. This is achieved by developing selective digital filtering within the software. In this paper data from damaged and undamaged bearings are compared on the basis of analyzing both rectified and unrectified signals.