Submarine Cable Vibration Signal Identification Method Based on VMD-BSA-SVM

Qiufeng Shang; Jiaxing Guo

doi:10.3788/LOP202259.1701001

Journals >Laser & Optoelectronics Progress >Volume 59 >Issue 17 >Page 1701001 > Article

Laser & Optoelectronics Progress
Vol. 59, Issue 17, 1701001 (2022)

Submarine Cable Vibration Signal Identification Method Based on VMD-BSA-SVM

Qiufeng Shang^1、2、3 and Jiaxing Guo^1、*

Author Affiliations

¹Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, Hebei , China

²Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding 071003, Hebei , China

³Baoding Key Laboratory of Optical Fiber Sensing and Optical Communication Technology, North China Electric Power University, Baoding 071003, Hebei , China

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DOI: 10.3788/LOP202259.1701001 Cite this Article Set citation alerts

Qiufeng Shang, Jiaxing Guo. Submarine Cable Vibration Signal Identification Method Based on VMD-BSA-SVM[J]. Laser & Optoelectronics Progress, 2022, 59(17): 1701001 Copy Citation Text

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Fig. 1. Flow chart of the VMD-BSA-SVM

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Fig. 2. Composition of the experimental system

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Fig. 3. Vibration signals. (a) Scouring signal; (b) anchor smashing signal; (c) friction signal

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Fig. 4. VMD results of the signal. (a) Scouring signal; (b) friction signal; (c) anchor smashing signal

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Fig. 5. Classification results of BSA-SVM. (a) Fitness curve; (b) accuracy of the test set

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Fig. 6. Classification results of different algorithms. (a) PSO-SVM algorithm; (b) GA-SVM algorithm; (c) GS-SVM algorithm

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IMF	Category	Scour	Friction	Anchor smashing
IMF 1	energy	0.1937	0.0087	0.0004
	kurtosis	0.1025	0.1601	0.2398
	energy entropy	0.318	0.0415	0.0029
IMF 2	energy	0.3739	0.0082	0.0006
	kurtosis	0.0492	0.1878	0.3074
	energy entropy	0.3678	0.0396	0.0044
IMF 3	energy	0.0458	0.0121	0.0006
	kurtosis	0.2292	0.1301	0.1807
	energy entropy	0.1411	0.0533	0.0045
IMF 4	energy	0.0009	0.0208	0.0019
	kurtosis	0.2507	0.2868	0.2198
	energy entropy	0.0061	0.0805	0.0121
IMF 5	energy	0.0007	0.3804	0.0038
	kurtosis	0.2793	0.1445	0.0211
	energy entropy	0.0053	0.3677	0.0212
IMF 6	energy	0.385	0.5697	0.9927
	kurtosis	0.0889	0.0907	0.0313
	energy entropy	0.3675	0.3205	0.0073

Table 1. Signal characteristics

Algorithm	Accuracy /%	Running time /s
BSA-SVM	99.17	67.32
GA-SVM	97.50	74.96
PSO-SVM	96.67	81.84
GS-SVM	95.83	49.27

Table 2. Comparison results of four types of algorithms

Qiufeng Shang, Jiaxing Guo. Submarine Cable Vibration Signal Identification Method Based on VMD-BSA-SVM[J]. Laser & Optoelectronics Progress, 2022, 59(17): 1701001

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