A weighted dissimilarity index to isolate faults during alarm floods

A fault-isolation method based on pattern matching using the alarm lists raised by the SCADA system during an alarm flood is proposed. A training set composed of faults is used to create fault templates. Alarm vectors generated by unknown faults are classified by comparing them with the fault templates using an original weighted dissimilarity index that increases the influence of the few alarms relevant to diagnose the fault. Different decision strategies are proposed to support the operator in his decision making. The performances are evaluated on two sets of data: an artificial set and a set obtained from a highly realistic simulator of the CERN Large Hadron Collider process connected to the real CERN SCADA system. •Fault vectorial templates are extracted from alarm lists raised by the faults.•Weights that evaluate the relevance of each alarm in the fault template are computed.•A weighted index measures the dissimilarity between a vector and a fault template.•Different fault isolation strategies to assist the operator are proposed.•The performances are evaluated on a data set from a Large Hadron Collider process.