Abstract
Remote diagnosis of industrial and manufacturing facilities constitutes a feasible alternative at high-risk or remote sites where unmanned operation is preferred. Computer-aided diagnostic tools can reduce downtime by providing support to remote monitoring centers and on-site plant operators. This paper describes a novel technique to perform on-line remote monitoring and diagnosis of industrial and manufacturing systems based on Bayesian belief networks and genetic algorithms. An implementation of the methodology in a chemical process industry is presented and potential applications for different types of industrial systems are discussed. >
Sign-in/Register to access full text options 
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.
