Structure learning methods for Bayesian networks to reduce alarm floods by identifying the root cause

Abstract

In times of increasing connectivity, complexity and automation safety is also becoming more demanding. As a result of these developments, the number of alarms for the individual operator increases and leads to mental overload. This overload caused by alarm floods is an enormous safety risk. By reducing this risk, it is not only possible to increase the safety for humans and machines, but also to correct the failure at an early stage. This saves money and reduces outage time. In this paper we present an approach using a Bayesian network to identify the root cause of an alarm flood. The root cause is responsible for a sequence of alarms. The causal dependencies between the alarms are represented with a Bayesian network, which serves as a causal model. Based on this causal model the root cause of an alarm flood can be determined using inference. There exist different methods to learn the structure of a Bayesian network. To investigate which method suites the best for the purpose of alarm flood reduction, one algorithm from each method is selected. We evaluated these algorithms with a dataset, which is recorded from a demonstrator of a manufacturing plant in the SmartFactoryOWL.