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Fault diagnosis is a major concern of the prognostics and health management of rotating machinery. Current practice in fault diagnosis is often challenged by the non-normality, multimodality, and nonlinearity of machinery health monitoring signals and their extracted features. A single classifier used in fault diagnosis fails when all these challenges exist. Thus, in this paper a hybrid ensemble learning...
Gearboxes are crucial devices in rotating power transmission systems with applications in a variety of industries. Gearbox faults can cause catastrophic physical consequences, long equipment downtimes, and severe production costs. Several artificial neural networks, learning algorithms, and feature selection methods have been used in the diagnosis of the gearbox healthy state. Given a specific gearbox,...
Traditional support vector machine needs pre-assumed kernel functions. This paper proposes a method via gene expression programming to automatically construct the kernel. The contributions of this paper include: (1) proposing the concepts of GEP kernel and kernel tree; (2) proposing the properties of GEP kernel and the kernel relation theorem; (3) proposing GEP based support vector machine (KGEP-SVM),...
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