Feature cognitive model combined by an improved variational mode and singular value decomposition for fault signals

Abstract

A feature cognitive model combined with an improved variational mode and singular value decomposition is presented to recognise the characteristics of the fault signals from vibration signals of mechanical equipment in this study. Specifically, the variational mode model is constructed firstly to decompose the known fault signals for mechanical equipment with the same load. Singular value decomposition approach is applied to recognise further the inherent modal features of the fault signals and construct the feature set. The supervised learning-support vector machine and the unsupervised learning-fuzzy c-means clustering are used to verify the effectiveness of the presented method. Finally, the provided feature cognitive model is used to recognise the bearing faults to verify its effectiveness. From simulation results, it can be seen that compared to the complete integration empirical mode decomposition method, the feature cognitive model combined by an improved variational mode and singular value decomposition can obtain more higher accuracy and larger evaluation coefficients. It is worth mentioning that the presented method can also be applied to recognise the key characteristics of the other signals.