Autonomous mining for alarm correlation patterns based on time-shift similarity clustering in manufacturing system

Current alarm systems employed in manufacturing applications are ambiguous in terms of indicating the root causes of process disturbances, which causes many difficulties for decision making. As manufacturing systems increase in complexity and scale, the continued reliance on human operators for alarm information management is impossible. A computer-aided information management system would increase analytical capability for alarm analysis. To this effect, an autonomous data mining method is introduced to search historical alarm logs for the correlations that can represent causal relationships, which can aid alarm management and system improvement. A hierarchical clustering method is used to carry out the correlation pattern search. Moreover, the similarity function of this method is designed to identify certain pre-defined correlation patterns. This method is validated in a vertical turning machine center alarm system application. The proposed method can discover a large number of alarm correlations, which are usually neglected by operators, and manage the alarms in the way that clarify process disturbance and enable rapid root cause analysis