# American Institute of Mathematical Sciences

August & September  2019, 12(4&5): 1471-1487. doi: 10.3934/dcdss.2019101

## EMD and GNN-AdaBoost fault diagnosis for urban rail train rolling bearings

 1 State Key Lab of Rail Traffic Control & safety, Beijing Jiaotong University, Beijing 100044, China 2 School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China 3 Institute of Computing Technology, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China

* Corresponding author: Guoqiang Cai

Received  July 2017 Revised  January 2018 Published  November 2018

Rolling bearings are the most prone components to failure in urban rail trains, presenting potential danger to cities and their residents. This paper puts forward a rolling bearing fault diagnosis method by integrating empirical mode decomposition (EMD) and genetic neural network adaptive boosting (GNN-AdaBoost). EMD is an excellent tool for feature extraction and during which some intrinsic mode functions (IMFs) are obtained. GNN-AdaBoost fault identification algorithm, which uses genetic neural network (GNN) as sub-classifier of the boosting algorithm, is proposed in order to address the shortcomings in classification when only using a GNN. To demonstrate the excellent performance of the approach, experiments are performed to simulate different operating conditions of the rolling bearing, including high speed, low speed, heavy load and light load. For de-nosing signal, by EMD decomposition is applied to obtain IMFs, which is used for extracting the IMF energy feature parameters. The combination of IMF energy feature parameters and some time-domain feature parameters are selected as the input vectors of the classifiers. Finally, GNN-AdaBoost and GNN are applied to experimental examples and the identification results are compared. The results show that GNN-AdaBoost offers significant improvement in rolling bearing fault diagnosis for urban rail trains when compared to GNN alone.

Citation: Guoqiang Cai, Chen Yang, Yue Pan, Jiaojiao Lv. EMD and GNN-AdaBoost fault diagnosis for urban rail train rolling bearings. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1471-1487. doi: 10.3934/dcdss.2019101
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##### References:
The process flow for applying the GNN-AdaBoost algorithm
Procedure for rolling bearing fault diagnosis based on EMD and GNN-AdaBoost
Simulator stand for rolling bearing faults
Different health conditions of the rolling bearing: (a) normal; (b) inner-race fault; (c) outer-race fault; (d) rolling ball fault
Time-domain waveform figures of four rolling bearing states under the working condition of 6 r/s speed and light load: (a) normal; (b) inner-race fault; (c) outer-race fault and (d) rolling ball fault.
Diagram of the 13 IMF components
Total error and weight changes
Error variations and iterations of GNN-AdaBoost
The testing results of GNN-AdaBoost and GNN under different working conditions: (a) 6 r/s and light load; (b) 6 r/s and heavy load; (c) 8 r/s and light load (d) 8 r/s and heavy load
Expected output code
 Fault type Expected output code Normal (1 0 0 0) Inner-race fault (0 1 0 0) Outer-race fault (0 0 1 0) Rolling ball fault (0 0 0 1)
 Fault type Expected output code Normal (1 0 0 0) Inner-race fault (0 1 0 0) Outer-race fault (0 0 1 0) Rolling ball fault (0 0 0 1)
Accuracy comparisons between GNN-AdaBoost and GNN under different working conditions
 Experimental condition GNN-AdaBoost GNN Right Wrong Accuracy (%) Right Wrong Accuracy (%) Speed 6 r/s, light load 77 3 96.25 71 9 88.75 Speed 6 r/s, heavy load 78 2 97.5 74 6 92.5 Speed 8 r/s, light load 78 2 97.5 73 7 91.25 Speed 8 r/s, heavy load 79 1 98.75 76 4 95
 Experimental condition GNN-AdaBoost GNN Right Wrong Accuracy (%) Right Wrong Accuracy (%) Speed 6 r/s, light load 77 3 96.25 71 9 88.75 Speed 6 r/s, heavy load 78 2 97.5 74 6 92.5 Speed 8 r/s, light load 78 2 97.5 73 7 91.25 Speed 8 r/s, heavy load 79 1 98.75 76 4 95
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