A Gear Fault Detection Method Based on a Fiber Bragg Grating Sensor

Chen Yong; Chen Yawu; Liu Zhiqiang; Liu Huanlin

doi:10.3788/CJL202047.0304007

Chinese Journal of Lasers, Volume. 47, Issue 3, 304007(2020)

A Gear Fault Detection Method Based on a Fiber Bragg Grating Sensor

Chen Yong^1、*, Chen Yawu¹, Liu Zhiqiang¹, and Liu Huanlin²

¹Key Laboratory of Industrial Internet of Things & Network Control, Ministry of Education,Chongqing University of Posts and Telecommunications, Chongqing 400065, China

²Key Laboratory of Optical Fiber Communication Technology, Chongqing University of Posts andTelecommunications, Chongqing 400065, China

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Figures & Tables(14)

Fig. 1. Simulated signal and its components. (a) Pulse signal; (b) 50 Hz signal; (c) 20 Hz signal; (d) composite signal

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Fig. 2. IMF components obtained by different algorithms. (a) EMD algorithm; (b) ANCEEMD algorithm

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Fig. 3. Gearbox fault detection platform

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Fig. 4. Simplified structure of lathe gear box

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Fig. 5. Six different states of 3# gear. (a) Normal; (b) mild wear; (c) severe wear;(d) pitting; (e) crack; (f) broken teeth

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Fig. 6. Gearbox fault detection flow chart

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Fig. 7. Time domain signal diagrams of 3# gear in different states. (a) Normal; (b) mild wear; (c) severe wear; (d) pitting; (e) crack; (f) broken teeth

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Fig. 8. IMF components and their frequency domain distribution under normal condition. (a) IMF component; (b) frequency domain distribution of the corresponding components

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Fig. 9. IMF components and their frequency domain distribution under broken condition. (a) IMF component; (b) frequency domain distribution of the corresponding components

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Table 1. Numerical table of three evaluation indicators
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Table 1. Numerical table of three evaluation indicators
Indicator IMF1 IMF2 IMF3 IMF4 IMF5 IMF6 Mean
P 0.0194 0.7055 0.7251 -0.0021 -0.0190 0.0025 0.2386
K 27.0810 1.5490 1.5683 4.1677 2.9263 1.4472 6.4546
T_indx 0.5254 1.0929 1.1372 -0.0089 -0.0555 0.0036 0.4491

Table 2. Gear parameter table
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Table 2. Gear parameter table
Gear 1# 2# 3# 4# 5# 6# 7#
Number of teeth 80 20 80 20 40 25 60
Reference diameter /mm 80 20 80 20 40 25 60
Module 1 1 1 1 1 1 1
Angle of pressure /(°) 20 20 20 20 20 20 20

Table 3. Fault type identification result of 3# gear
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Table 3. Fault type identification result of 3# gear
Method Rate of identification /%
Normal Mild wear Severe wear Pitting Crack Broken teeth
EMD+SVM 73 60 80 87 90 93
EEMD+SVM 82 77 85 90 91 96
CEEMD+SVM 89 90 93 92 91 96
ANCEEMD+BP 85 90 92 89 92 97
ANCEEMD+SVM 93 92 94 95 97 99

Table 4. Fault type identification result of 4# gear
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Table 4. Fault type identification result of 4# gear
Method Rate of identification /%
Normal Mild wear Severe wear Pitting Crack Broken teeth
EMD+SVM 66 70 71 85 87 90
EEMD+SVM 71 65 80 89 92 94
CEEMD+SVM 88 87 90 92 92 94
ANCEEMD+BP 90 87 84 87 93 96
ANCEEMD+SVM 96 93 90 99 95 99

Table 5. Fault type identification result of 6# gear
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Table 5. Fault type identification result of 6# gear
Method Rate of identification /%
Normal Mild wear Severe wear Pitting Crack Broken teeth
EMD+SVM 78 83 85 80 86 91
EEMD+SVM 81 83 88 84 87 96
CEEMD+SVM 84 87 91 93 95 94
ANCEEMD+BP 84 74 90 89 93 95
ANCEEMD+SVM 99 95 96 93 97 98

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Chen Yong, Chen Yawu, Liu Zhiqiang, Liu Huanlin. A Gear Fault Detection Method Based on a Fiber Bragg Grating Sensor[J]. Chinese Journal of Lasers, 2020, 47(3): 304007

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