Spectrum Shape Based Roller Bearing Fault Detection and Identification

This article describes a combination of numerical methods of automated fault detection and identification in rolling bearings, even when there is a limited knowledge about inspected bearings and their characteristic frequencies in particular. The most important feature is global approach to solving the stated problem using several rolling bearing diagnostic methods and techniques which work simultaneously and complement each other. These include the well-known Hilbert-based envelope, Discrete Wavelet Transform and Fourier Transform as well as new Outer Envelope Filtering, Pulse Train Scanning, spectrum shape analysis and novel application of known signal processing tools like Fuzzy Logic and Continuous Wavelet Transform. Combination of partial results obtained by each path allows for judging the probabilities of different fault types (outer-race, inner-race and roller). Two techniques additionally used here are spectrum scanning by a train of harmonic pulses (Pulse Train Scanning) and an outer envelope filter which removes high frequency noise, but enhances the most important short time diagnostic symptoms, which are often removed by standard filtering. A combination of such solutions allows for unambiguous and precise localization of characteristic fault frequencies in a spectrum without initial knowledge about their expected values.