Accelerated multiple alarm flood sequence alignment for abnormality pattern mining. (October 2019) - Lai, Shiqi; Yang, Fan; Chen, Tongwen; Cao, Liang
Abstract: Alarm floods can interfere with operators and may therefore cause or aggravate industrial accidents. A novel algorithm is proposed for pattern mining in multiple alarm floods. Unlike traditional methods which either cannot deal with multiple sequences with time stamps or suffer from high computational cost, the computational complexity of this proposed algorithm is reduced significantly by introducing a generalized pairwise sequence alignment method and a progressive multiple sequence alignment approach. Two types of alignment refinement methods are developed to improve the alignment accuracy. The effectiveness of the proposed algorithm is tested using a dataset from a real chemical plant.