Designing a lightweight 1D convolutional neural network with Bayesian optimization for wheel flat detection using carbody accelerations

ABSTRACT

Many freight waggons in Europe have been recently equipped with embedded systems (ESs) for vehicle tracking. This provides opportunities to implement the real-time fault diagnosis algorithm on ESs without additional investment. In this paper, we design a 1D lightweight Convolutional Neural Network (CNN) architecture, i.e. LightWFNet, guided by Bayesian Optimization for wheel flat (WF) detection. We tackle two main challenges. (1) Carbody acceleration has to be used for WF detection, where signal-to-noise ratio is much lower than at axle box level and thus the WF detection is much more difficult. (2) ESs have very limited computation power and energy supply. To verify the proposed LightWFNet, the field data measured on a tank waggon under operational condition are used. In comparison to the state-of-the-art lightweight CNNs, LightWFNet is validated for WF detection by using carbody accelerations with much lower computational costs.

KEYWORDS:

• Wheel flat
• fault diagnosis
• machine learning
• convolutional neural network
• Bayesian optimization

Acknowledgments

The experiment data used in this paper are supported by the previous projects of Chair of Rail Vehicles TUB. The research is funded by the EU Shift2Rail project Assets4Rail (Grand number: 826250) under Horizon 2020 Framework Programme.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Horizon 2020 Framework Programme [826250 — Assets4Rail — H2020-S2RJU-2018].