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.

通过零样本学习方法，本文提出了一种新的故障检测和状态监测方法，可以在没有故障样本的情况下，对工业系统进行可靠的故障检测与健康状况评估。通过使用变分自编码器捕捉先前观察到和未知条件的概率分布，在隐空间比较每个样本与正常操作的参考分布的偏差，建立健康指数的阈值，能够高准确度地检测严重的未知故障，从而提升工业领域的故障检测能力，增强安全性和优化维护实践。