Abstract
In this paper, authors present the original procedure for local damage detection in rolling bearings based on vibration data. The aim is to obtain envelope spectrum (ES) of the signal component related to damage, that is clear and easy to interpret. The method is especially aimed at cases, when multiple cyclic impulsive components are present and interfere with each other, which makes ES of such signal very difficult to evaluate. In order to deal with such situation properly, authors propose to choose Cyclic Spectral Coherence (CSC) map as a two-dimensional data representation that will be the basis for the analysis. Nonnegative Matrix Factorization (NMF) is used to analyze such map in two ways: first, it helps to initially separate cyclic components by producing a set of filters for input vibration data, and second, to identify proper damage-related frequency components in envelope spectrum. In addition, an intermediate step of spatial denoising allows enhancing the properties of CSC map. Finally, Monte Carlo simulation improves statistical significance of the result and increases robustness by reducing the impact of random initialization effects. The method has been evaluated using real-life vibration data measured on rolling bearing operating in the industrial gas compressor.
Published Version
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