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Improving alarm management in manufacturing: a clustering approach for deriving insights from Abb´s error logs
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Resumo(s)
In manufacturing, effective alarm management is critical for maintaining high production efficiency.
This research applies advanced clustering techniques to analyze error logs from ABB's
socket outlet production line. It compares BERTopic, hierarchical clustering, and supervised clustering
to identify patterns and trends in alarm data. BERTopic emerged as the most effective
method, providing coherent and meaningful clusters. The analysis revealed significant insights
into recurring alarm issues, critical alarms, and their impact on production metrics. These findings
can help ABB enhance efficiency through targeted interventions. Recommendations include advanced
alarm filtering, real-time monitoring systems, and combining data-driven insights with expert
knowledge.
Descrição
Palavras-chave
Alarm management Bertopic Hierarchical clustering Supervised clustering CEMS MIM