Fault detection and diagnosis (FDD) models play an important role in the development of any CM system. Vibration analysis is widely used as an efficient condition monitoring (CM) tool for rotating machines in various industries. Experimental results on several datasets show that the proposed PM-U-TSVM not only achieves higher classification accuracy but also has better generalization performance when dealing with noisy classification problems.
Furthermore, the kernel extension of the PM-U-TSVM is proposed to deal with the nonlinear case. This joint learning strategy can eventually help our model perform better in terms of effectiveness and robustness. Specifically, in contrast to the classic SVM-type methods that need to solve a single large-scale QPP problem, the proposed PM-U-TSVM extends twin SVM learning model by determining a pair of smaller size non-parallel parameter margin hyperplanes to provide a more flexible parametric-margin structure for input data, and analyzes the prior information ensconced in Universum to fully exploit the latent useful knowledge to construct the final classifier.
Motivated by the merit of twin support vector machine (TSVM), this paper presents an improved parametric-margin Universum twin support vector machine (PM-U-TSVM), which utilizes the prior knowledge contained in the Universum samples to improve the classification performance and exploits the parametric-margin strategy to be suitable for error structure to enhance the representation ability of the TSVM.
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