Signal Processing Tools for Tracking the Size of a Spall in a Rolling Element Bearing

Abstract

There is considerable interest in diagnostics and prognostics of operating machines based on vibration analysis and signal processing, because the major economic benefit from condition-based monitoring comes from being able to predict with reasonable certainty the likely lead time before breakdown. In the case of rolling element bearings, a number of powerful techniques have been developed in recent years to separate the rather weak signals coming from faulty bearings from strong background vibrations, and to diagnose the type of fault. The MED (minimum entropy deconvolution) technique was initially applied to bearings to reduce the overlap of adjacent impulse responses in high speed bearings and thus allow their diagnosis by envelope analysis. It was then suspected that the technique also might have the potential to separate the impulses from entry into, and exit from an individual fault, and thus give information on the fault size. This paper gives the results of an initial study into the application of MED, and other techniques, to obtain the best measure of the length of a developing spall, to use in prognostic algorithms to estimate safe remaining life, based on current size and rate of evolution with time. It was found that the response to the entry and exit events was markedly different, so considerable pre-processing was required before the MED could be applied. The paper also discusses a number of methods to reduce noise and obtain an averaged estimate of the spall length.