Nianchao LIU, Qin LI, Xiaoting ZHAO, Sheng LIANG. Cluster-based recognition method for Φ-OTDR system's vibration signals[J]. Infrared and Laser Engineering, 2024, 53(11): 20240294
- Infrared and Laser Engineering
- Vol. 53, Issue 11, 20240294 (2024)
Fig. 1. Schematic diagram of Φ -OTDR
Fig. 2. Flowchart of identification task
Fig. 3. Flowchart of secondary data cropping
Fig. 4. (a) Original data graph of manual percussion; (b) Original data graph of machine excavation; (c) Cropped data graph of manual percussion; (d) Cropped data graph of machine excavation; (e) Envelope value graph of manual percussion; (f) Envelope value graph of machine excavation
Fig. 5. Correlation matrix of raw data
Fig. 6. (a) Three-dimensional projection of the original data after dimensionality reduction; (b) Three-dimensional plot of the effect of agglomerative clustering
Fig. 7. Line charts of normalized time-domain characteristics of wind noise. (a) Line chart with 15 characteristic values; (b) Bc and Bi feature values; (c) Bmax , Brm , Ben , Bvar , Bstd , Bff , Bmeans , and Bav feature values
Fig. 8. Line charts of normalized time-domain characteristics for manual knocking. (a) Line chart with 15 characteristic values; (b) Ben , Bff , Bmax , Bav , Bmeans , Bstd , and Brm feature values
Fig. 9. Line charts of normalized time-domain characteristics for machine excavation. (a) Line chart with 15 characteristic values; (b) Feature values of Bmeans and Bav ; (c) Feature values of Ben , Bstd , Bff , Brm , and Bmax
Fig. 10. Correlation matrix of clipped and normalized data
Fig. 11. Elbow diagrams for agglomerative clustering of cropped normalized data
Fig. 12. Under K =3 condition (a) 3D projection of cropped normalized data; (b) 3D projection of agglomerative clustering of cropped normalized data
Fig. 13. Evaluation coefficients for agglomerative clustering under K =3 condition
Fig. 14. Confusion matrix for agglomerative clustering under K = 3 condition
Fig. 15. Under the condition of K =2 and truth labels of 0 and 1 (a) 3D projection of cropped normalized data; (b) 3D projection of agglomerative clustering of cropped normalized data
Fig. 16. Confusion matrix for agglomerative clustering under the condition of K =4
Fig. 17. (a) Plot of rainfall data; (b) Plot of windblown data; (c) Plot of direct tapping data; (d) Plot of indirect tapping data
Fig. 18. Cluster evaluation coefficients of this study and previous studies
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Table 1. Characterization of the significance of the seven time-domain features
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Table 2. Cluster assessment indicators
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Table 3. Parameters of devices in experiment
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Table 4. Time domain features corresponding to symbols
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Table 5. Number of input samples and number of clusters for various events
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Table 6. Number of input samples, number of samples correctly clustered and accuracy under different K conditions
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Table 7. Number of samples for three correct clusters under different K conditions
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