Deep subdomain generalisation network for health monitoring of high-speed train brake pads

Hu, Ruohui; Zhang, Min; Xing, Meng; Kang, Zhuang

doi:10.1016/j.engappai.2022.104896

Cited by 12 publications

(1 citation statement)

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“…Through principal component analysis and feature fusion, redundant information about important feature sets is eliminated and the fault recognition rate and training efficiency are improved. Hu et al 11 applied the subdomain generalization method to monitor the health of the friction pads with different uneven wear status in the brake pads. These studies have conducted status monitoring research on the train braking system and brake pads, and they have achieved excellent results, but did not consider the braking status monitoring under variable working conditions.…”

Section: Introductionmentioning

confidence: 99%

Health status monitoring of high-speed train brake pads considering noise under variable working conditions

Kang

Zhang

et al. 2023

Structural Health Monitoring

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The brake pads of high-speed trains operate under complex and variable conditions, and the collected brake signals are easily affected by noise, making monitoring the health status of brake pads more difficult. A multi-representation adaptation network for online monitoring the health status of high-speed train brake pads, which are affected by noise under variable working conditions, is proposed in this study. First, a parameter-sharing deep residual network is used to extract the friction block features of the source and target domain data. Then, the features are mapped to different low-dimensional feature spaces through the inception adaptation module, and multiple representations are obtained. The network applies conditional maximum mean discrepancy to align representations of the source and target domains, thus learning multiple domain-invariant representations. Hence, the network acquires more knowledge of the friction block status and attenuates the interference of noise signals on the status monitoring. The friction block vibration data were collected from various brake disc speeds, and variable working condition-transfer experiments under the influence of noise were performed on the brake friction and bearing datasets. The results show that the proposed network outperforms other transfer methods, which can better extract and identify the status features of the friction block under the noise interference.

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Section: Introductionmentioning

confidence: 99%