Application of graph theory and filter based variable selection methods in the design of a distributed data-driven monitoring system

Abstract

Two methods that represent extensions of our previously developed methods for distributed data-driven monitoring are proposed. The first, Extended Forward Selection for Distributed Pattern Recognition, selects a decomposition for distributed pattern recognition such that diagnostic performance is near optimal subject to constraints. It uses a filter method to select sensors and allocates them among a minimum number of subsystems using graph theoretic algorithms. Its advantage over the Forward Selection for Distributed Pattern Recognition method is that it scales to systems with sensors in the order of 1,000. The second method, Extended Subsystem and Sensor Allocation, uses graph theoretic algorithms to find the minimum number of locations for distributed monitoring, the sensors that should transmit to each location, and the monitoring tasks at each location. Its main advantage over the original Subsystem and Sensor Allocation method is that it is applicable even when data is not available before plant operation begins.