  With the introduction of damage tolerance-based design philosophies, the
demand for reliable and robust structural health monitoring (SHM) procedures
for aerospace composite structures is increasing rapidly. The performance of
supervised learning algorithms for SHM depends on the amount of labeled and
balanced datasets. Apart from this, collecting datasets accommodating all
possible damage scenarios is cumbersome, costly, and inaccessible for aerospace
applications. In this paper, we have proposed two different
unsupervised-feature learning approaches where the algorithms are trained only
on the baseline scenarios to learn the distribution of baseline signals. The
trained unsupervised feature learner is used for delamination prediction with
an anomaly detection philosophy. In the first approach, we have combined
dimensionality reduction techniques (principal component analysis and
independent component analysis) with a one-class support vector machine. In
another approach, we have utilized deep learning-based deep convolutional
autoencoders (CAE). These state-of-the-art algorithms are applied on three
different guided wave-based experimental datasets. The raw guided wave signals
present in the datasets are converted into wavelet-enhanced higher-order
representations for training unsupervised feature-learning algorithms. We have
also compared different techniques, and it is seen that CAE generates better
reconstructions with lower mean squared error and can provide higher accuracy
on all the datasets.
