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Journals >Acta Optica Sinica >Volume 43 >Issue 5 >Page 0506001 > Article

Acta Optica Sinica
Vol. 43, Issue 5, 0506001 (2023)

φ-OTDR Pattern Recognition Based on LSTM-CNN

Ming Wang¹, Zhou Sha¹, Hao Feng^1、*, Lipu Du², and Dunzhe Qi²

Author Affiliations

¹State Key Laboratory of Precision Measuring Technology and Instruments, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China

²Ningxia Hui Autonomous Region Water Conservancy Engineering Construction Center, Yinchuan 750004, Ningxia , China

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DOI: 10.3788/AOS221468 Cite this Article Set citation alerts

Ming Wang, Zhou Sha, Hao Feng, Lipu Du, Dunzhe Qi. φ-OTDR Pattern Recognition Based on LSTM-CNN[J]. Acta Optica Sinica, 2023, 43(5): 0506001 Copy Citation Text

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Structure diagram of φ-OTDR

Fig. 1. Structure diagram of φ-OTDR

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Comparison of the data presentation form between the time-space map and the time-domain curve

Fig. 2. Comparison of the data presentation form between the time-space map and the time-domain curve

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Fig. 3. Operation process of CNN and LSTM. (a) 1D convolution; (b) 2D convolution; (c) LSTM

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Fig. 4. Schematic of the internal structure of φ-OTDR integrated system

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Fig. 5. Structure diagram of BRNN (LSTM)

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Fig. 6. Training situation and test results of LSTM. (a) Curve of loss function with dynamic learning rate; (b) curve of loss function with fixed learning rate; (c) background noise; (d) excavation signal; (e) motor vibration signal; (f) walking signal

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Fig. 7. STFT results of target signals. (a) Background noise; (b) excavation signal; (c) motor vibration signal; (d) walking signal

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Fig. 8. Network structure of LSTM-CNN

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Fig. 9. Comparison of overall evaluation indicators. (a) Train accuracy; (b) test accuracy; (c) train loss; (d) test loss

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Fig. 10. Recall comparison of test set. (a) Background noise; (b) excavation signal; (c) motor vibration signal; (d) walking signal

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Fig. 11. Precision comparison of test set. (a) Background noise; (b) excavation signal; (c) motor vibration signal; (d) walking signal

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Fig. 12. Precision of validation sample. (a) Background noise; (b) excavation signal; (c) motor vibration signal; (d) walking signal

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Fig. 13. Recall of validation sample. (a) Background noise; (b) excavation signal; (c) motor vibration signal; (d) walking signal

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Fig. 14. Precision of validation sample of models in Table 3. (a) Background noise; (b) excavation signal; (c) motor vibration signal;(d) walking signal

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Fig. 15. Recall of validation sample of models in Table 3. (a) Background noise; (b) excavation signal; (c) motor vibration signal; (d) walking signal

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Net number	Net structure	Input	Number of CNN layers	Layer node	Learning rate
1	ANN	Time-domain sequence	0	36	0.01
2	ANN	STFT	2	80	0.05
3	ANN	Time-domain sequence + STFT	2	88	0.03
4	CNN	Time-domain sequence + STFT	4	90	0.001
5	LSTM-CNN	Time-domain sequence + STFT	4	90	0.001

Table 1. Important parameters of different neural network structures

Sample type	Number of sample points				Accuracy / %
Sample type	Noise	Excavation	Motor vibration	Walking	Accuracy / %
Original	327	218	478	710
Net 1	252	167	720	468	35.77
Net 2	340	180	550	663	82.28
Net 3	136	140	815	373	47.39
Net 4	328	193	493	695	89.47
Net 5	315	224	493	695	94.43

Table 2. Distribution of identification results and accuracy of the verification samples

Model type	Model 1	Model 2	Model 3	Model 4	Model 5
Structure	LSTM-CNN	SVM	KNN	Decision-tree	Random-forest
Test accuracy /%	94.60	83.65	83.71	78.17	87.38
Validation accuracy /%	94.43	81.98	65.66	75.24	83.26

Table 3. Comparison of LSTM-CNN and common machine-learning models

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References (23)

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Ming Wang, Zhou Sha, Hao Feng, Lipu Du, Dunzhe Qi. φ-OTDR Pattern Recognition Based on LSTM-CNN[J]. Acta Optica Sinica, 2023, 43(5): 0506001

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