Multiple criteria intelligence tracking for detecting extremes from sequences of risk incidents

A number of quantitative methods have emerged to identify and track precursors to risk for engineering systems. While data mining and statistical inference identify patterns from information of historical events, they may not address features of extreme events that have never occurred. While, event and fault-tree analyses synthesize important information on basic and initiating risk events, they fall short of addressing incident data in real time. Accident precursor analyses refine event and fault tree analyses by considering near-misses or precursors from system operational data. Complementing precursor analysis is an existing method of detecting anomalies in a sequence of risk incident reports to (a) identify and count patterns in the reports, (b) measure and track the complexity of the reports with univariate statistical process control, and (c) identify specific periods of instability. This paper extends the existing method to (d) introduce two additional measurements of patterns, (e) apply multiple criteria statistical process control to track the multiple measurements of the reports, and (f) use optimal search parameters to generate a watch list of system components for input to accident precursor analyses. The extension is demonstrated for a sequence of four observation periods of incident reports in a power distribution system.