An approach for diagnosability analysis and sensor placement for continuous processes based on evolutionary algorithms and analytical redundancy

Abstract

In this work we propose to use an approach based on genetic algorithms to obtain analytical redundancy relations to study the diagnosability property on a given con-tinuous system, and if this not fulfill, our approach allows studying the sensor place-ment problem in order to fulfill it. The redundancy relations are based on the mini-mal test equation support and in a structural analysis over a bipartite graph. The faults analysis is studied using a multi-objective fitness function in two genetic al-gorithms which describe the different constraints to be covered in order to reach the diagnosability property on the system. Additionally, our approach allows studying the sensors placement problem on systems that do not fulfill the detectability or isolability properties, using another genetic algorithm. © 2015 Rubén Leal et al.