  With the rising demands for robust structural health monitoring procedures
for aerospace structures, the scope of intelligent algorithms and learning
techniques is expanding. Supervised algorithms have shown promising results in
the field provided a large, balanced, and labeled amount of data for training.
For some applications like aerospace, the data collection process is
cumbersome, time-taking, and costly. Also, generating possible damage scenarios
in a laboratory setup is challenging because of the complexity of the damage
initiation and failure mechanism. Besides this, the uncertainties of the
real-time operation restrict the online prediction accuracy with supervised
learning. In this paper, deep generative models are proposed for unsupervised
delamination prediction as an anomaly detection problem. In this
one-class-based model, the deep learning network is trained to learn the
distribution of baseline signals. In the testing phase, damage signals and
unseen baseline signals are fed into the trained network to predict the state
of the structure, i.e., healthy or unhealthy (delamination). It is seen that
the proposed method can successfully predict the delamination with high
accuracy.
