Dynamic Alarm Causality Analysis Method Integrating Monitoring Contribution and Transfer Entropy

Alarm flooding poses a significant safety hazard in industry. It is of paramount importance to uncover the causality behind alarm flooding at its early stages. On one hand, limited data samples make it challenging to accurately determine the causal relationships among alarms. On the other hand, a massive volume of data samples may lead to delays in causal inference. This paper proposes a Contribution and Transfer-entropy based Dynamic Alarm Causality analysis method (CT-DAC) to address this. CT-DAC reconstructs the contribution of each monitored variable to the dynamic anomalies in the system, enabling dynamic causal relationship mining based on limited data. This approach offers a faster and more accurate solution for identifying the initial alarm causality triggering alarm flooding problems. Experimental work conducted on the Tennessee Eastman Process demonstrates the superior performance of the CT-DAC method.