A correlation consistency based multivariate alarm thresholds optimization approach. (November 2016) - Gao, Huihui; Liu, Feifei; Zhu, Qunxiong
Abstract: Different alarm thresholds could generate different alarm data, resulting in different correlations. A new multivariate alarm thresholds optimization methodology based on the correlation consistency between process data and alarm data is proposed in this paper. The interpretative structural modeling is adopted to select the key variables. For the key variables, the correlation coefficients of process data are calculated by the Pearson correlation analysis, while the correlation coefficients of alarm data are calculated by kernel density estimation. To ensure the correlation consistency, the objective function is established as the sum of the absolute differences between these two types of correlations. The optimal thresholds are obtained using particle swarm optimization algorithm. Case study of Tennessee Eastman process is given to demonstrate the effectiveness of proposed method. Highlights: The alarm thresholds optimization is investigated in this manuscript. The Data-driven ISM is used to reveal the interaction between variables. The Particle Swarm Optimization is applied to obtain the optimal thresholds. The TE process is used to verify the effectiveness of the proposed method.