Cyclic sources extraction from complex multiple-component vibration signal via periodically time varying filter

The problem of local damage detection is widely discussed in the literature. There are many methods which can be applied, however there is still a need for new techniques addressing specific diagnostic issues. In particular, the case of complex multiple-component vibration signal is a challenging problem. In this paper we focus on such a problem related to a gearbox operating in industrial conditions. Our method consists of several stages. First we transform signal to time-frequency domain using spectrogram. Then for each frequency bin we apply a novel procedure which indicates location of cyclic impulses in given time series. This algorithm is based on the periodically distributed local maxima detection and quantification of their significance. Such procedure requires a priori known fault frequency. If the machine might reveal multiple fault, the procedure has to be calculated separately for each fault frequency. A time-varying filter is designed using the indicated local maxima comprised in the score matrix. Then, signals representing each fault frequency are obtained using inverse short-time Fourier transform algorithm. The method is illustrated with application to simulated and real data from complex mining machine - heavy duty gearbox.