| Summary: | This paper proposes a three-stage fault diagnosis strategy for multistage centrifugal pumps. First, the proposed method identifies and selects fault characteristic modes of vibration to overcome the substantial noise produced by other unrelated macro-structural vibrations. In the second stage, raw hybrid statistical features are extracted from the fault characteristic modes of vibration in time, frequency, and the time-frequency domain. These extracted features result in a high-dimensional feature space. However, in general, not all of the features are best to characterize the ongoing processes in a centrifugal pump, and some of the extracted features might be irrelevant or even redundant, which can affect the fault classification capabilities of the classification algorithm. In the third stage, a novel dimensionality reduction technique, called Pearson Linear Discriminant Analysis (PLDA), is introduced. PLDA assesses the helpfulness of the feature parameters. This technique selects highly interclass-correlated features and adds them to a helpful feature pool. To achieve maximum intraclass separation while maintaining the original class information, linear discriminant analysis is then applied to the helpful feature pool. This combination of helpful feature pool formation and linear discriminant analysis forms the proposed application of PLDA. The reduced discriminant feature set obtained from PLDA is then classified using the k-nearest neighbor classification algorithm. The proposed method outperforms the previously presented methods in terms of classification accuracy.
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