An Improved Jaccard Coefficient-Based Clustering Approach with Application to Diagnosis and RUL Estimation
Sample clustering techniques play a crucial role in the data-driven state evaluation of electromechanical equipment, and selecting an appropriate similarity measurement method for sample sets helps improve the clustering performance. The Jaccard coefficient is a commonly employed indicator of simila...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | IET Signal Processing |
| Online Access: | http://dx.doi.org/10.1049/2024/6586622 |
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| Summary: | Sample clustering techniques play a crucial role in the data-driven state evaluation of electromechanical equipment, and selecting an appropriate similarity measurement method for sample sets helps improve the clustering performance. The Jaccard coefficient is a commonly employed indicator of similarity for scalar set-type samples. In this paper, we propose an incremental clustering algorithm for matrix-type samples by defining an improved Jaccard coefficient. First, a new binary relation is formulated to derive a relationship matrix between samples. Second, an undirected graph is given by using the relationship matrix, and an improved pruning operation is provided to simplify the graph by eliminating redundant edges. Then, a new relationship matrix is generated according to the modified graph, which enables the calculation of the improved Jaccard coefficient. By using the improved Jaccard coefficient, the improved incremental clustering algorithm updates cluster centers by selecting a particular sample to maximize the sum of similarities between the selected sample and other samples within the same cluster. Finally, the effectiveness of the proposed incremental clustering algorithm is demonstrated in fault diagnosis and remaining useful life estimation application scenarios, respectively. The experimental results indicate that the improved algorithm outperforms traditional clustering methods. |
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| ISSN: | 1751-9683 |