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Development of an Anomaly Detection Algorithm for Predictive Maintenance in Lyophilization Systems
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This paper focuses on the development of a diagnostic tool for predictive maintenance in industrial lyophilization systems. At the core of the system is an anomaly detection algorithm designed to identify abnormal patterns in operational data. Through its development, key diagnostic strategies have been identified to improve system reliability and early failure detection, ultimately aiming to reduce downtime and operational costs.

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Facilities & Equipment Quality
Adv Manuf QA