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

PROJECT TITLE :

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

ABSTRACT:

This paper proposes a replacement sensor deployment strategy primarily 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 bugs and sensor readings. A multi-objective optimization is performed to facilitate the monitoring of single-station multistep producing method (SMMP). A stream of fault info model is built to explain the propagation of fault state in the SMMP. By suggests that of state-area transformation, a detection issue is used to supply the initial sensor deployment. The optimal sensor deployment in an SMMP is achieved by an improved shuffled frog leaping algorithm (ISFLA), that minimizes the fault unobservability, maximizes the system stability, and minimizes the value for the whole system, below the constraints on detectability, stationarity, and limited resources. 2 experimental investigations on an assembly unit and a manufacturing unit are conducted to verify the methodology. Comparative studies demonstrate that the proposed QCEG is ready 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 higher than the popular integer programming in managing the multi-objective optimization problem.

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