Application of principal component analysis of time-frequency representation for gearbox fault detection

Abstract

Dimensionality reduction methods are very useful and effective tools in the field of data analytics, used either independently or as a pre-processing step in the frames of a complex algorithm. In this paper a simple yet powerful technique for local damage detection in heavy-duty industrial machinery is presented with particular focus on gearboxes. It assumes that the cyclic component present in the vibration signal carrying information about the damage, can be extracted from relevant frequency bands of the signal. Although this assumption is usually a starting point for selective filtration in the notion of Informative Frequency Band (IFB) identification, in this case the frequency bands are not addressed directly. The authors propose to apply Principal Component Analysis (PCA) as a dimensionality reduction method on the time-frequency representation of the input data in such a way, that the dimension of frequency is reduced. In this way, the variance maximized in the first principal component is expected to capture the cyclic information which is related to the damage present in the machine.