Condition Monitoring with Incomplete Data: An Integrated Variational Autoencoder and Distance Metric Framework

arXiv:2404.05891v1 Announce Type: cross
Abstract: 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. …

abstract arxiv autoencoder availability challenges cs.ai cs.lg data eess.sp framework incomplete data industrial maintenance monitoring paper planning safety samples solution systems type world