Optimal Sensor Deployment for Manufacturing Process Monitoring Based on Quantitative Cause-Effect Graph

Abstract

This paper proposes a new sensor deployment strategy based on quantitative cause-effect graph (QCEG) to handle the heterogeneity among the properties of sensors and faults. A QCEG is developed to model the cause-effect relationship between the system faults and sensor readings. A multi-objective optimization is performed to facilitate the monitoring of single-station multistep manufacturing process (SMMP). A stream of fault information model is built to describe the propagation of fault state in the SMMP. By means of state-space transformation, a detection factor is used to provide the initial sensor deployment. The optimal sensor deployment in an SMMP is achieved by an improved shuffled frog leaping algorithm (ISFLA), which minimizes the fault unobservability, maximizes the system stability, and minimizes the cost for the whole system, under the constraints on detectability, stationarity, and limited resources. Two experimental investigations on an assembly unit and a manufacturing unit are conducted to verify the methodology. Comparative studies demonstrate that the proposed QCEG is able to overcome the shortcomings of directed graph (DG) in handling sensor heterogeneity and multiple objectives. As a goal-oriented swarm-intelligence search strategy, the ISFLA performs better than the popular integer programming in dealing with the multi-objective optimization problem.