Hybrid HC-PAA-G3K for novelty detection on industrial systems

Zhang, Yu, Chen, Jun and Gallimore, Michael (2014) Hybrid HC-PAA-G3K for novelty detection on industrial systems. In: International Conference on Advanced Technology & Sciences (ICAT'14), 12-15 August, 2014, Antalya, Turkey.

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Item Type:Conference or Workshop contribution (Presentation)
Item Status:Live Archive


Piecewise aggregate approximation (PAA) provides a powerful yet computationally efficient tool for dimensionality reduction and feature extraction. A new distance-based hierarchical clustering (HC) is now proposed to adjust the PAA segment frame sizes. The proposed hybrid HC-PAA is validated by a generic clustering method ‘G3Kmeans’ (G3K). The efficacy of the hybrid HC-PAA-G3K methodology is demonstrated using an application case study based on novelty detection on industrial gas turbines. Results show the hybrid HC-PAA provides improved performance with regard to cluster separation, compared to traditional PAA. The proposed method therefore provides a robust algorithm for feature extraction and novelty detection. There are two main contributions of the paper: 1) application of HC to modify conventional PAA segment frame size; 2) introduction of ‘G3Kmeans’ to improve the performance of the traditional K-means clustering methods.

Keywords:Piecewise aggregate approximation, Hierarchical clustering, G3Kmeans, Novelty detection, Industrial gas turbine
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
Divisions:College of Science > School of Engineering
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ID Code:14704
Deposited On:19 Aug 2014 11:25

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