APPLICATION OF STATISTICAL MOMENTS TO BEARING FAILURE-DETECTION

Failure of rolling element bearings plays a significant role in the br eakdown of industrial machinery. However, the analysis of signals resu lting from measurements taken from outer casings of equipment has prov en to be an effective and powerful tool for the early detection of fai lure 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 develo pment. This paper looks at variations of the statistical moment analys is method that show potential for damage detection at a much earlier s tage. 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 m icro-computer using suitable software. An important part of the proces sing is separating out unwanted data from other energy sources within the machine. This is achieved by developing selective digital filterin g within the software. In this paper data from damaged and undamaged b earings are compared on the basis of analyzing both rectified and unre ctified signals.