Remote diagnosis and monitoring of complex industrial systems using a genetic algorithm approach

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