The Feature Vector Mapping of Process Plant's Topology Structure Based on Statistical Learning

• Published in: 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) pp. 521 - 531
• Main Authors: Qin, Li, Tang, Wei-Qing, Li, Shi-Cai, Tao, Qing-Hua
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2018
• Subjects: feature vector; process plant; similarity; statistical learning; topology structure
• DOI: 10.1109/CCIS.2018.8691324

The feature vector mapping of process plant's topology structure is one of the key problem in process plant topology similarity calculation. High quality feature vector does not only need to convert the expression from graph to length fixed vector, but also should mine and express the topology semantics that under the connective relationships. A novel method has been presented in this paper to solve this problem. We convert the topology graph to observation sequences according to the reacting processes with path extracting algorithm; then reference the state sequences from observation sequences with HMM, this can remove the node dependencies and mine the latent topology semantics; at last, reduce the state sequences to a length fixed vector with LDA model. The background and basis definitions has been introduced in this paper firstly, some related work has been also described; then we analyzed the characteristics of process plants' topology structure and present the details of the mapping method; finally, the results have been evaluated. According to the manually judgement by process industry professionals, the method is proved to be practical and effective.