Accelerated multiple alarm flood sequence alignment for abnormality pattern mining
| Autor: | Liang Cao, Tongwen Chen, Shiqi Lai, Fan Yang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Multiple sequence alignment Computational complexity theory Flood myth business.industry Computer science Sequence alignment Pattern recognition 02 engineering and technology Industrial and Manufacturing Engineering Computer Science Applications ALARM 020901 industrial engineering & automation 020401 chemical engineering 13. Climate action Control and Systems Engineering Modeling and Simulation Pairwise sequence alignment Artificial intelligence 0204 chemical engineering Abnormality business |
| Zdroj: | Journal of Process Control. 82:44-57 |
| ISSN: | 0959-1524 |
| DOI: | 10.1016/j.jprocont.2019.06.004 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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