Condition Monitoring with Incomplete Data: An Integrated Variational
  Autoencoder and Distance Metric Framework
Maryam Ahang Mostafa Abbasi Todd Charter Homayoun Najjaran
Condition monitoring of industrial systems is crucial for ensuring safety and
maintenance planning, yet notable challenges arise in real-world settings due
to the limited or non-existent availability of fault samples. This paper
introduces an innovative solution to this problem by proposing a new method for
fault detection and condition monitoring for unseen data. Adopting an approach
inspired by zero-shot learning, our method can identify faults and assign a
relative health index to various operational conditions. Typically, we have
plenty of data on normal operations, some data on compromised conditions, and
very few (if any) samples of severe faults. We use a variational autoencoder to
capture the probabilistic distribution of previously seen and new unseen
conditions. The health status is determined by comparing each sample's
deviation from a normal operation reference distribution in the latent space.
Faults are detected by establishing a threshold for the health indexes,
allowing the model to identify severe, unseen faults with high accuracy, even
amidst noise. We validate our approach using the run-to-failure IMS-bearing
dataset and compare it with other methods. The health indexes generated by our
model closely match the established descriptive model of bearing wear,
attesting to the robustness and reliability of our method. These findings
highlight the potential of our methodology in augmenting fault detection
capabilities within industrial domains, thereby contributing to heightened
safety protocols and optimized maintenance practices.