Novel method of informative frequency band selection for vibration signal using Nonnegative Matrix Factorization of spectrogram matrix

Abstract The problem of local damage detection in rotating machines is currently the highly important subject of interest in the field of condition monitoring. In the literature one can find many different strategies. One of the most common approaches is the vibration signal analysis aiming at informative frequency band selection. In case of simply structured signals classic methods (e.g. spectral kurtosis) are sufficient and return clear information about the damage. However, in real-world cases the signal is usually much more complicated. Indeed, such signals consist of many different components, for instance: damage-related cyclic impulses, heavy-tailed background noise etc. Hence, there is a growing need for robust damage detection methods. In this paper a novel method of informative frequency band selection is proposed. It utilizes the approach of Non-negative Matrix Factorization applied to time-frequency signal representation. The described algorithm is evaluated using simulated signal containing several different components, that resembles real-life vibration signal from copper ore crusher, as well as real-life signal measured on the crusher. Using the obtained structure of informative frequency band it is possible to filter particular components out of the original signal.