  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.
