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arXiv · 2207.03758

Virtual Axle Detector based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network

Steven Robert Lorenzen·Henrik Riedel·Maximilian Michael Rupp·Leon Schmeiser·Hagen Berthold·Andrei Firus·Jens Schneider
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Abstract

In the practical application of the Bridge Weigh-In-Motion (BWIM) methods, the position of the wheels or axles during the passage of a vehicle is in most cases a prerequisite. To avoid the use of conventional axle detectors and bridge type specific methods, we propose a novel method for axle detection through the placement of accelerometers at any point of a bridge. In order to develop a model that is as simple and comprehensible as possible, the axle detection task is implemented as a binary classification problem instead of a regression problem. The model is implemented as a Fully Convolutional Network to process signals in the form of Continuous Wavelet Transforms. This allows passages of any length to be processed in a single step with maximum efficiency while utilising multiple scales in a single evaluation. This enables our method to use acceleration signals at any location of the bridge structure serving as Virtual Axle Detectors (VADs) without being limited to specific structural types of bridges. To test the proposed method, we analysed 3787 train passages recorded on a steel trough railway bridge of a long-distance traffic line. Our results on the measurement data show that our model detects 95% of the axes, thus, 128,599 of 134,800 previously unseen axles were correctly detected. In total, 90% of the axles can be detected with a maximum spatial error of 20cm, with a maximum velocity of $v_{\mathrm{max}}=56,3~\mathrm{m/s}$. The analysis shows that our developed model can use accelerometers as VADs even under real operating conditions.

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Steven Robert Lorenzen, Henrik Riedel, Maximilian Michael Rupp, Leon Schmeiser, Hagen Berthold, Andrei Firus, Jens Schneider. 2022-07-08. Virtual Axle Detector based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network. https://doi.org/10.3390/s22228963

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