  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.
