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A Software Process Workflow for Smart Anomaly Detection Systems

Abstract
The use of smart anomaly detection systems is set to increase at organisations during the Industry 4.0 era, for use in Predictive Maintenance (PdM). The European Spallation Source (ESS) serves as a representative organization in this study where a novel software process workflow is proposed to facilitate the effective implementation of robust anomaly detection systems. The research addresses the software engineering (SE) aspects of these systems, offering valuable insights for software engineers and researchers interested in predictive anomaly detection (PAD). Employing a design science research approach, the study identifies current challenges and proposes potential solutions for developing these systems at the ESS facility. The proposed SE workflow aims to modularize engineering efforts, thereby enabling the creation of highquality systems, and contributing to the advancement of rigorous software systems for anomaly detection.
Degree
Student essay
URI
https://hdl.handle.net/2077/78206
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  • Kandidatuppsatser
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Thesis (1.601Mb)
Date
2023-08-16
Author
Gouws, Vernita
Keywords
anomaly detection
predictive maintenance
machine learning
workflow
software engineering process
lifecycle
Language
eng
Metadata
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