基于自适应神经模糊网络的转辙机故障诊断方法

doi:10.13229/j.cnki.jdxbgxb.20220893

Abstract

Abstract:

The number of railway signal switch machines is large， the working environment is bad， and many factors lead to the high frequency of equipment failure. In order to realize the accurate fault diagnosis of railway switch machine， a fault diagnosis method of switch machine based on dynamic weight particle swarm optimization and adaptive neural fuzzy network is proposed by analyzing the vibration signals generated during the switch machine operation. Firstly， the set empirical mode decomposition algorithm was used to decompose the vibration signals into several intrinsic mode functions and screen them. Then， the improved time-domain multi-scale spread entropy algorithm was used to extract the eigenentropy of IMFs， and then input the optimized ANFIS model to learn the fault diagnosis. Finally， it is compared with a variety of diagnostic model algorithms and learning algorithms. The experimental results show that the proposed method can effectively diagnose the fault of the switch machine， and has certain reference significance for the intelligent fault diagnosis of the switch machine and related research in the future.

Key words: switch machine, fault diagnosis, dynamic particle swarm optimization, adaptive neural fuzzy inference network system

CLC Number:

TP391.5

Yong-gang CHEN,Ji-ye XU,Hai-yong WANG,Wen-xiang XIONG. Fault diagnosis method of point machine based on adaptive neural fuzzy inference network system[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(11): 3274-3280.

Figures/Tables 11

Fig.1

Table 1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Table 2

Evaluation of diagnostic results of different models"

模型	$R 2$	$e R M S E$
BP	0.791	0.3257
k-NN	0.882	0.1632
ANFIS	0.957	0.0814

Table 2

Table 3

Diagnostic classification results of different optimization methods"

学习算法	迭代次数	迭代时长/s	$e R M S E$
GA	74	13.6	0.2382
Grid	173	31.8	0.1643
PSO	132	24.5	0.1138
DPSO	102	18.3	0.0814

Table 3

Table 4

Diagnostic results of different data preprocessing methods"

预处理方法	特征提取算法	$e R M S E$
EMD	IMDE	0.2211
EMD	ITMDE	-
EEMD	IMDE	0.0526
EEMD	ITMDE	0.0312

Table 4

References 10

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编号	工况描述	数量	时长/s
1	正常工作状态	80	6.5~7.3
2	转换力过小	84	6.6~7.2
3	转换力过大1级	83	6.5~7.5
4	转换力过大2级	79	6.3~7.1
5	固定部件松动	87	6.5~7.5
6	空载	96	6.8~7.7
7	卡异物	90	7.2~8.2
8	卡缺口	86	5.8~6.3

Fault diagnosis method of point machine based on adaptive neural fuzzy inference network system

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