  Data-driven fault diagnostics of safety-critical systems often faces the
challenge of a complete lack of labeled data associated with faulty system
conditions (i.e., fault types) at training time. Since an unknown number and
nature of fault types can arise during deployment, data-driven fault
diagnostics in this scenario is an open-set learning problem. Most of the
algorithms for open-set diagnostics are one-class classification and
unsupervised algorithms that do not leverage all the available labeled and
unlabeled data in the learning algorithm. As a result, their fault detection
and segmentation performance (i.e., identifying and separating faults of
different types) are sub-optimal. With this work, we propose training a
variational autoencoder (VAE) with labeled and unlabeled samples while inducing
implicit supervision on the latent representation of the healthy conditions.
This, together with a modified sampling process of VAE, creates a compact and
informative latent representation that allows good detection and segmentation
of unseen fault types using existing one-class and clustering algorithms. We
refer to the proposed methodology as "knowledge induced variational autoencoder
with adaptive sampling" (KIL-AdaVAE). The fault detection and segmentation
capabilities of the proposed methodology are demonstrated in a new simulated
case study using the Advanced Geared Turbofan 30000 (AGTF30) dynamical model
under real flight conditions. In an extensive comparison, we demonstrate that
the proposed method outperforms other learning strategies (supervised learning,
supervised learning with embedding and semi-supervised learning) and deep
learning algorithms, yielding significant performance improvements on fault
detection and fault segmentation.
