Automated search of process control limits for fault detection in time series data. (September 2022)

• Record Type: Journal Article
• Authors: Schlegl, Thomas; Tomaselli, Domenico; Schlegl, Stefan; West, Nikolai; Deuse, Jochen
• Is Part Of: Journal of process control. Volume 117(2022)
• Page Start: 52
• Page End: 64
• Publication Date: 2022-09
• Subjects: Process control -- Control limits -- Fault detection -- Time series classification; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.07.002
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: Manually defined control limits remain a common strategy for quality control in manufacturing due to their ease of deployment on the shop floor compared to more advanced data analysis approaches. Despite their continued importance, there is no systematic method of defining these control limits. However, sub-optimal control limits can lead to undetected faults or cause unnecessary interruption to production. This manuscript presents an algorithm that systematizes this manual process into an efficient search task. We conceptualized the search task as a sequence of sub-problems that are based on the conventional steps taken by process experts when defining control limits. This algorithm can be integrated into an expert tool for shop floor personnel to automate the definition of control limits in annotated time series data. We demonstrate the efficacy of the control limits found by our algorithm by comparing them to those manually defined by process experts in real-world process data from the automotive industry. Furthermore, we show that our algorithm generalizes to traditional time series classification problems and achieves state-of-the-art performance on selected benchmark datasets. Our work is the first effort in automating the otherwise manual definition of control limits for fault detection. Highlights: Summary of existing time series classification methods and their practical feasibility for productive deployment. Presentation of the algorithm including a brief introduction into the theoretical fundamentals of the underlying statistical techniques. Experimental evaluation and validation of the algorithm on public datasets and real-world manufacturing data. Publication of the source code to facilitate adoption by practitioners and researchers.