Abstract
Machine fault classification is an important task for intelligent identification of the health patterns for a mechanical system being monitored. Effective feature extraction of vibration data is very critical to reliable classification of machine faults with different types and severities. In this paper, a new method is proposed to acquire the sensitive features through a combination of local discriminant bases (LDB) and locality preserving projections (LPP). In the method, the LDB is employed to select the optimal wavelet packet (WP) nodes that exhibit high discrimination from a redundant WP library of wavelet packet transform (WPT). Considering that the obtained discriminatory features on these selected nodes characterize the class pattern in different sensitivity, the LPP is then applied to address mining inherent class pattern feature embedded in the raw features. The proposed feature extraction method combines the merits of LDB and LPP and extracts the inherent pattern structure embedded in the discriminatory feature values of samples in different classes. Therefore, the proposed feature not only considers the discriminatory features themselves but also considers the dynamic sensitive class pattern structure. The effectiveness of the proposed feature is verified by case studies on vibration data-based classification of bearing fault types and severities.
Highlights
Machine fault classification is an important task for intelligent identification of the condition patterns for the system being monitored
It can be seen that the clustering evaluation metrics S, ACC, and normalized mutual information (NMI) of the local discriminant bases (LDB) feature are higher than those of the wavelet packet transform (WPT) feature, and locality preserving projections (LPP) performs much better than principal component analysis (PCA)
This paper presents a feature extraction method which integrates the LDB and the LPP to explore the useful and
Summary
Machine fault classification is an important task for intelligent identification of the condition patterns for the system being monitored. Manifold learning pursuits the goal to embed data that originally lies in a high-dimensional space in a lower dimensional space while preserving local characteristic properties, for example, local geometric property (Isomap [28]), local embedding structure (LLE [29]), local adjacency relations (LE [30]), and local tangent space information (LTSA [31]) These nonlinear manifold learning methods have been effectively developed to machine fault classification, they need heavy computation cost and are complex to be extended for fault classification of a new data [25,26,27, 32]. In this paper, based on energy features of the nodes selected by LDB algorithm from the WP library, a new effective feature is proposed to mine the nonlinear pattern information by LPP in the case of bearing fault classification.
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