Parallel Theory-Based Risk Assessment for Networked Manufacturing Systems

The increasing applications of robots and other automated machines into intelligent manufacturing poses great requirement of timely risk evaluation and recognition, which is critical to the stable operation of a factory. However, only a little previous work is centered on this problem. In this paper, we present a close-loop manufacturing situation management framework base on parallel theory to address the challenge of risk recognition in real time. A graph illustrates the relationships between various factors in networked manufacturing, followed by the introduction of corresponding risk measurement indices. And then the fuzzy analytic hierarchy process (FAHP) method is applied to assess the risk of the manufacturing. The factor with the maximum risk is selected out with max-max algorithmic operators and is input into the real system for checking out. The innovation of this paper lie in the proposition of a real time risk analytic framework for operational streamlines base on parallel theory and the integration of network graph with FAHP method for risk evaluation. The numerical testing results show that the model is valid, and its corresponding evaluation method can quickly recognize the highest risk occurred in the manufacturing. The risk evaluation framework and method can be generalized to other networked and complex systems, e.g., transportation systems.