Multiple event identification and characterization by retrospective analysis of structured data streams

The sensors installed in complex systems generate massive amounts of data, which contain rich information about a system’s operational status. This article proposes a retrospective analysis method for a historical data set, which simultaneously identifies when multiple events occur to the system and characterizes how they affect the multiple sensing signals. The problem formulation is motivated by the dictionary learning method and the solution is obtained by iteratively updating the event signatures and sequences using ADMM algorithms. A simulation study and a case study of the steel rolling process validate our approach. The supplementary materials including the appendices and the reproduction report are available online.