Abstract
Early prediction of an incoming alarm flood sequence can provide valuable information to industrial operators, so as to facilitate taking corrective actions in time. A real-time pattern matching and ranking approach is proposed in this work to conduct similarity analysis under an online alarm flood situation and to export the results as a ranking list of historical alarm flood sequences. Unit and set-based pre-matching mechanisms are proposed to remove irrelevant sequences, and a set-based indexing and extension strategy is applied to further avoid unnecessary computation. Real-time decision supports in the form of ranking of similar historical alarm flood sequences are presented to the industrial operators. The ranking list will be updated with the increasing of the online alarm flood sequence. Moreover, the industrial operators, with real-time assistance, can predict the incoming alarm sequence and will be pro-active in identifying the potential problems. The effectiveness of the proposed method is demonstrated by an industrial case study based on real alarm & event logs from a refinery plant.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
