An adaptive frequency optimization method for credibility assessment of equipment digital twins in intelligent manufacturing

To guarantee the effective equipment digital twin (DT) applications in intelligent manufacturing, the DT’s credibility must be properly assessed continuously. Pursuing a rather high assessment frequency would guarantee the accuracy of the credibility but would slow down the quantification process or consume too much computational resources. The dynamic credibility assessment of the real-time evolving DTs has comprehensive requirements for accuracy, rapidity, and cost. To achieve the dynamic balance, an adaptive assessment frequency optimization method is proposed. The optimal assessment frequency can be adjusted in real time using particle swarm optimization algorithms according to variable application requirements and working conditions. The influence of different frequency on the credibility of digital twins are analyzed based on experience from VV&A (Verification, Validation and Accreditation) methods in M&S (Modeling and Simulation). The method not only guarantees the accuracy and effectiveness of the assessment results, but also meets the assessment rapidity requirements and is relatively more economical.