Optimal Sensor Deployment to Diagnose Large-Scale Manufacturing Systems Using a Convergence-Trajectory Controlled Ant Colony System Algorithm

Abstract

Abstract In this article, the problem of optimal sensor deployment in large-scale manufacturing systems for effective process monitoring is solved using a variant of the ant colony system (ACS) algorithm to obtain an optimal number of sensors, their types, and locations for monitoring various possible faults. For this purpose, first, we define the need for optimizing sensor deployment in large-scale manufacturing processes because of the increasing application of Wireless Sensor Networks (WSNs) as an architectural framework for Machine-to-Machine (M2M) communications and Cyber-Physical Systems (CPS). Then a multi-objective formulation of optimal sensor deployment in a Single Station Multi-Step Manufacturing Process concerning sensor costs, system reliability, and stability is briefly explained. As noted earlier by several authors, the sensor deployment problem is a set covering problem. It is known that metaheuristics like genetic algorithms, variants of ant colony algorithms, etc. are not efficient in finding a near-optimal solution in less computational budget to the large-scale set covering problems. For this purpose, a Convergence Trajectory Controlled ant colony system is developed and applied on a case study of an automated assembly robot. For an effective demonstration of the convergence power of the developed algorithm, we also apply our algorithm on some large-scale benchmark datasets of the set covering problem and compare the results obtained with the ant colony system algorithm. The results obtained show that the developed algorithm can give a near-optimal solution in less computational budget than the ACS algorithm.