Abstract:

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.

Key words: alarm flood sequence, discrete Fourier transform, similarity, cluster analysis

摘要：

报警泛滥是过程工业报警管理中普遍存在且难以解决的问题。报警泛滥序列聚类分析有助于报警根源分析和预警。针对目前报警泛滥序列相似度测量方法存在的缺陷,如对报警序列长度限制、计算复杂、依赖参数,采用基于离散傅里叶变换的方法在频域对报警泛滥序列进行相似性分析,提出了离散傅里叶功率谱的欧氏距离作为度量报警泛滥序列相似度距离的方法,计算不同报警泛滥的相似度距离,再通过非加权组平均法获得报警泛滥序列的聚类树状图,根据相似度距离,确定报警泛滥的模式,帮助操作员确定异常根源,做出快速响应。TE仿真过程在不同干扰下的应用实例验证了该方法的有效性、准确性。

关键词: 报警泛滥序列, 离散傅里叶变换, 相似度, 聚类分析