Stockwell Transform Combined with Subdomain Adaptation for Escalator Motor Bearing Transfer Diagnosis

doi:10.12141/j.issn.1000-565X.220758

Abstract

Abstract:

In the absence of sufficient escalator motor bearing failure data, to address the issue of unstable bearing fault characteristics during frequent load and speed variations in escalator operation, this paper proposed a transfer diagnosis method for escalator motor bearings using Stockwell (S) transformation combined with subdomain adaptation. Firstly, for the fault characteristics of escalator motor bearings, a time-frequency image of vibration signals was generated using the S transform combined with bilinear interpolation. This time-frequency image effectively reflects bearing fault features and is subsequently aligned with the requirements of the feature extraction network. Secondly, local maximum mean discrepancy (LMMD) was introduced at the output end of the feature extraction network layer based on the deep residual neural network ResNet-50. It incorporates the confidence of bearing fault sample categories as weights in the mapped maximum mean discrepancy (MMD), aligning the dis-tributions of subdomains belonging to the same category, thereby expanding the scope of transfer learning. Next, the network was constructed to minimize both LMMD and cross-entropy loss functions, and network training was performed using mini-batch gradient descent. Consequently, by refining the feature differences between different fault categories, fault subdomain self-adaptation was achieved, overcoming the problem of low transfer diagnosis accuracy. Finally, based on two publicly available bearing fault datasets and a limited amount of escalator motor bearing fault data, the S-transformed time-frequency dataset was constructed, and transfer diagnosis experiments were conducted. The results demonstrate that the proposed method achieves an average accuracy of 99.1% and 95.49% for transfer diagnosis in two different source-to-target domain scenarios of escalator bearings, outper-forming five commonly used diagnostic methods in terms of recognition accuracy and robustness.

Key words: fault diagnosis, time-frequency analysis, transfer learning, bearing

CLC Number:

TH17

CHEN Zhong, TANG Xin, ZHANG Daming, et al. Stockwell Transform Combined with Subdomain Adaptation for Escalator Motor Bearing Transfer Diagnosis[J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(12): 34-41.

Figures/Tables 9

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数据集	健康状态	样本数	轴承型号
A	内圈故障	4 096	SKF6205
	外圈故障	4 096
	滚子故障	4 096
	正常状况	4 096
B	内圈故障	1 280	ER16K
	外圈故障	1 280
	滚子故障	1 280
	正常状况	1 280
C	内圈故障	900	NSK6314
	外圈故障	900
	滚子故障	900
	正常状况	900

健康状态	数据集A	数据集B	数据集C
F_i
F_o
F_b
Nor

方法	平均准确率/%	准确率标准差
CWT-A	100.00	0.00
ST-A	100.00	0.00
CWT-B	98.86	0.38
ST-B	99.61	0.12

方法	AB		AC
方法	平均准确率/%	准确率标准差	平均准确率/%	准确率标准差
ResNet50	42.63	6.42	29.33	10.11
DDC	78.86	4.23	41.56	9.13
DAN	86.35	4.27	62.42	6.17
DDAN	94.47	1.80	53.50	2.25
DANN	97.42	1.05	86.89	2.89
本研究提出的方法	99.10	0.90	95.49	3.16

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