A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
Integrating Expert Knowledge with Domain Adaptation for Unsupervised Fault Diagnosis
[article]
2021
arXiv
pre-print
Data-driven fault diagnosis methods often require abundant labeled examples for each fault type. On the contrary, real-world data is often unlabeled and consists of mostly healthy observations and only few samples of faulty conditions. The lack of labels and fault samples imposes a significant challenge for existing data-driven fault diagnosis methods. In this paper, we aim to overcome this limitation by integrating expert knowledge with domain adaptation in a synthetic-to-real framework for
arXiv:2107.01849v1
fatcat:hrz4pn73sfbvzpcy4ih5xr6n5m