Separation of multiple local-damage-related components from vibration data using Nonnegative Matrix Factorization and multichannel data fusion

The problem of local damage detection in case of analyzing vibration signal from rotating machines is mostly related to the detection of periodic impulsive components. Depending on various features of the signal, this task can be relatively simple in some cases (e.g. for an impulsive component in the presence of Gaussian noise). However, in our case multiple impulsive components occupying with the overlapping frequency bands are present. For all components, the impulses can be periodic. In this article, authors present a novel methodology based on data fusion from multichannel vibration data from heavy-duty industrial gearbox operating in the driving station of a belt conveyor. The proposed method is based on the factorization of spectrograms using Generalized Hierarchical Alternating Least Squares Nonnegative Matrix Factorization with Beta-Divergence (later referred to as beta-HALS NMF). Partial information obtained from the factorization is fused into a single data set for each impulsive component present in the signal. Finally, Griffin-Lim algorithm is used to estimate the complex phase layer of artificial spectrograms allowing to recover the near-perfect time series of each impulsive component extracted from the signal. This method has been tested on four-channel vibration data. (C) 2020 Elsevier Ltd. All rights reserved.