Towards Design Principles for a Real-Time Anomaly Detection Algorithm Benchmark Suited to Industrie 4.0 Streaming Data
|Herausgeber:||Bui, T.X.||Stichwörter:||Signal detection; % reductions; Algorithmics; Anomaly-detection algorithms; Design Principles; Production-machines; Real-time anomaly detections; Real-time detection; Self repair; Self-diagnose; Streaming data; Anomaly detection||Erscheinungsdatum:||2022||Herausgeber:||IEEE Computer Society||Journal:||Proceedings of the Annual Hawaii International Conference on System Sciences||Volumen:||2022-January||Startseite:||6323 – 6329||Zusammenfassung:||
The vision of Industrie 4.0 includes the automated reduction of anomalies in flexibly combined production machine groups up to a zero-failure ideal. Algorithmic real-time detection of production anomalies may build the basis for machine self-diagnosis and self-repair during production. Several real-time anomaly detection algorithms appeared in recent years. However, different algorithms applied to the same data may result in contradictory detections. Thus, real-time anomaly detection in Industrie 4.0 machine groups may require a benchmark ranking for algorithms to increase detection results' reliability. This paper makes a qualitative research contribution based on ten expert interviews to find design principles for such a benchmark ranking. The experts were interviewed on three categories, namely timeliness, thresholds and qualitative classification. The study's results can be used as groundwork for a prototypical implementation of a benchmark. © 2022 IEEE Computer Society. All rights reserved.
Cited by: 2; Conference name: 55th Annual Hawaii International Conference on System Sciences, HICSS 2022; Conference date: 3 January 2022 through 7 January 2022; Conference code: 187534
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checked on Dec 8, 2023