Discrete Fourier transform-based alarm flood sequence cluster analysis and applications in process industry

Alarm floods is a prevalent and difficult problem in alarm management of process industry. Alarm cluster analysis is helpful for alarm root cause analysis and alarm prediction. Aiming at the deficiencies of the current similarity measurement methods for alarm flood sequences, such as limitation of length of alarm sequences, computational complexity, depending on parameters, the discrete Fourier transform (DFT)-based method is employed to analysis on similarity among alarm flood sequences in the frequency domain. The Euclidean distance of the DFT power spectra of alarm flood sequences is proposed as a similarity distance metric for alarm floods, similarity distances of different alarm floods are evaluated. Dendrograms of alarm flood sequences by Unweighted Pair Group Method with Arithmetic mean (UPGMA) is obtained, according to similarity distance, determine the pattern of alarm floods and help operators identify the root cause of the abnormal for a rapid response. An application case of TE simulation process under different disturbances demonstrates validation and accuracy of the proposed method.