Detection of Correlated Alarms Using Graph Embedding

TL;DR

This paper introduces a novel AI-based graph embedding and clustering method to detect correlated alarms in industrial systems, aiming to improve efficiency and operator trust amid increasing alarm complexity.

Contribution

It presents a new approach combining graph embedding and clustering to identify correlated alarms, enhancing alarm management in complex industrial environments.

Findings

Effective detection of correlated alarms demonstrated on Tennessee-Eastman process

Reduces operator confusion by filtering redundant alarms

Improves system efficiency and trust in alarm systems

Abstract

Industrial alarm systems have recently progressed considerably in terms of network complexity and the number of alarms. The increase in complexity and number of alarms presents challenges in these systems that decrease system efficiency and cause distrust of the operator, which might result in widespread damages. One contributing factor in alarm inefficiency is the correlated alarms. These alarms do not contain new information and only confuse the operator. This paper tries to present a novel method for detecting correlated alarms based on artificial intelligence methods to help the operator. The proposed method is based on graph embedding and alarm clustering, resulting in the detection of correlated alarms. To evaluate the proposed method, a case study is conducted on the well-known Tennessee-Eastman process.