Abstract
It is shown how both pattern recognition methods (in the form of neural networks) and hidden Markov models (HMMs) can be used to automatically monitor online data for fault detection purposes. Monitoring for anomalies or faults poses some technical problems which are not normally encountered in typical HMM applications such as speed recognition. In particular, the ability to detect data from previously unseen classes and the use of prior knowledge in constructing the Markov model are both essential in applications of this nature. Recent progress on these and related topics in the context of fault detection is discussed. An application of these methods to the problem of online health monitoring of an antenna pointing system is described. >
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
