PID control loop performance assessment and diagnosis based on DEA-related MCDA

Control loop performance assessment and diagnosis have been attracting more and more attention in the academia and industry. Both traditional performance assessment method and minimum variance method often require the process model and provide limited information, which is not particularly convenient for practical applications. Therefore, the method based on data envelopment analysis (DEA)-related multiple criteria decision analysis (MCDA) is developed for assessing and diagnosing PID control loop performance, which relies solely upon the collected process data during routine plant operation. The control loop performance is assessed and sorted by utilizing the self-evaluation DEA-related MCDA model. The operation priority of the control loop is ranked and determined by utilizing the cross-evaluation DEA-related MCDA model. The improving direction and quantitative space of control loop performance can be diagnosed by DEA-related MCDA model with slack variables and non-Archimedean infinitesimal ε. The correctness and effectiveness of the proposed method are confirmed and validated by simulation examples.

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