Cold Start Approach for Data-Driven Fault Detection

  title={Cold Start Approach for Data-Driven Fault Detection},
  author={Mihajlo Grbovic and Weichang Li and Niranjan A. Subrahmanya and Adam K. Usadi and Slobodan Vucetic},
  journal={IEEE Transactions on Industrial Informatics},
A typical assumption in supervised fault detection is that abundant historical data are available prior to model learning, where all types of faults have already been observed at least once. This assumption is likely to be violated in practical settings as new fault types can emerge over time. In this paper we study this often overlooked cold start learning problem in data-driven fault detection, where in the beginning only normal operation data are available and faulty operation data become… CONTINUE READING
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