Detection of Correlated Alarms Using Graph Embedding
This addresses alarm inefficiency for industrial operators, but appears incremental as it builds on existing AI methods for a specific domain.
The paper tackled the problem of correlated alarms in industrial alarm systems, which reduce efficiency and confuse operators, by proposing a novel method based on graph embedding and alarm clustering, and evaluated it on the Tennessee-Eastman process.
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.