A Distributed Framework for Knowledge-Driven Root-Cause Analysis on Evolving Alarm Data-An Industrial Case Study - Wilch, Jan; Vogel-Heuser, Birgit; Mager, Jens; Cendelin, Rostislav; Fett, Thomas; Hsieh, Yu-Ming; Cheng, Fan-Tien (IEEE robotics and automation letters, 2023)
Root-cause Analysis (RCA) of alarms is a well-established research area in automated Production Systems (aPS). Many RCA algorithms have been proposed and successfully evaluated and new ones are being developed. Recently, researchers focus on the incorporation of formalized information about the technical process in the analysis to gather further evidence for common root causes. In industrial applications, alarm data are usually preprocessed to accommodate for use case-specific properties and prepare subsequent analysis steps. Consequently, this letter proposes a generalized RCA framework, for which an arbitrary number of preprocessing, data-driven RCA, and postprocessing algorithms can be selected, to support varying use cases. The framework was successfully evaluated in an industrial case study, using 1.8 million alarms recorded over 450 days from an industrial nonwoven production plant and analyzed using formalized information from process documentation and expert interviews. Seven preprocessing algorithms, one data-driven RCA algorithm, and nine postprocessing algorithms typical for continuous and hybrid technical processes were realized in an otherwise entirely use case-agnostic implementation.