TY - JOUR U1 - Wissenschaftlicher Artikel A1 - Evirgen, Safa A1 - Wartenberg, Maylin T1 - Steps and Challenges in Analyzing Real Sensor Data from a Productive Press Shop and its Value for Predictive Maintenance Application JF - International Journal on Cybernetics & Informatics N2 - This paper highlights the significance of AI-powered maintenance strategies in modern industry for operational optimization and reduced downtime. It emphasizes the crucial role of sensor data analysis in identifying anomalies and predicting failures. The research specifically examines sensor data from an automotive press shop, addressing questions related to data selection, collection challenges, and knowledge generation. By utilizing unsupervised learning on compressed air data from a press line, the study identifies patterns, anomalies, and correlations. The results offer insights into the potential for implementing an effective predictive maintenance strategy. Additionally, a systematic literature review underscores the importance of data analysis in production systems, particularly in the context of maintenance. KW - Predictive Maintenance KW - smart database KW - sensor data analytics KW - production KW - Prädiktive Instandhaltung KW - Datenbank KW - Sensor KW - Data Science Y1 - 2024 UN - https://nbn-resolving.org/urn:nbn:de:bsz:960-opus4-34401 SN - 2277-548X SS - 2277-548X U6 - https://doi.org/10.25968/opus-3440 DO - https://doi.org/10.25968/opus-3440 VL - 13 IS - 3 SP - 1 EP - 10 PB - Academy and Industry Research Collaboration Center (AIRCC) ER -