Neural network method for inversion of hard point height of medium and low speed maglev track

Qingqin Yang; Qi Zhang; Zhuo Zhang; Dixiang Chen; MengChun Pan; Wenwu Zhou; Yuan Ren; Hao Ma; Lihui Liu

doi:10.1117/12.2680740

31 May 2023 Neural network method for inversion of hard point height of medium and low speed maglev track

Qingqin Yang, Qi Zhang, Zhuo Zhang, Dixiang Chen, MengChun Pan, Wenwu Zhou, Yuan Ren, Hao Ma, Lihui Liu

Proceedings Volume 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023); 1270438 (2023) https://doi.org/10.1117/12.2680740
Event: 8th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2023), 2023, Hangzhou, China

Abstract

The maglev power supply system is realized by the loop formed by the contact rail. Hard points on the contact rail are the key factors that affect the continuity of power supply, and even affect the safe operation of the track in serious cases. The hard points height characteristic are related to the separation time and distance between the collector shoe and the contact rail. In order to explore the characteristics of the height data, a hard-points platform for simulating the contact rail is built. The characteristics of the acceleration signal passing through the simulated hard points platform are extracted by time-frequency analysis. Using the neural network model to explore the correlation, the regression prediction of the contact rail height value can be realized. Based on this method, the prediction error of simulating hard points inversion at a specific height is within 2%, and the effect is good. At present, it has been used in engineering practice, and it has played an important role in the detection and maintenance of the hard points of the medium and low speed maglev contact rail.

Citation Download Citation

Qingqin Yang, Qi Zhang, Zhuo Zhang, Dixiang Chen, MengChun Pan, Wenwu Zhou, Yuan Ren, Hao Ma, and Lihui Liu "Neural network method for inversion of hard point height of medium and low speed maglev track", Proc. SPIE 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023), 1270438 (31 May 2023); https://doi.org/10.1117/12.2680740

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KEYWORDS

Neural networks

Data modeling

Feature extraction

Signal detection

Time-frequency analysis

Error analysis

Sensors

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