  This study proposes a data-driven method that detects cable damage from
measured cable forces by recognizing biased patterns from the intact
conditions. The proposed method solves the pattern recognition problem for
cable damage detection through time series classification (TSC) in deep
learning, considering that the cable's behavior can be implicitly represented
by the measured cable force series. A deep learning model, long short term
memory fully convolutional network (LSTM-FCN), is leveraged by assigning
appropriate inputs and representative class labels for the TSC problem, First,
a TSC classifier is trained and validated using the data collected under intact
conditions of stay cables, setting the segmented data series as input and the
cable (or cable pair) ID as class labels. Subsequently, the classifier is
tested using the data collected under possible damaged conditions. Finally, the
cable or cable pair corresponding to the least classification accuracy is
recommended as the most probable damaged cable or cable pair. The proposed
method was tested on an in-service cable-stayed bridge with damaged stay
cables. Two scenarios in the proposed TSC scheme were investigated: 1) raw time
series of cable forces were fed into the classifiers; and 2) cable force ratios
were inputted in the classifiers considering the possible variation of force
distribution between cable pairs due to cable damage. Combining the results of
TSC testing in these two scenarios, the cable with rupture was correctly
identified. This study proposes a data-driven methodology for cable damage
detection that requires the least data preprocessing and feature engineering,
which enables fast and convenient early detection in real applications.
