Revolutionizing Fault Prediction in MetroPT Datasets: Enhanced Diagnosis and Efficient Failure Prediction through Innovative Data Refinement
Küçük Resim Yok
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This scientific paper presents groundbreaking advancements in Predictive Maintenance (PdM) within Industry 4.0, employing cutting-edge machine learning classification algorithms for fault prediction and diagnosis in Air Production Unit (APU) systems like MetroPT and MetroPT-3. This research uses data-driven methodologies to optimize feature extraction techniques to enhance fault prediction and improve diagnostic accuracy. A robust and versatile model emerges through comprehensive testing, displaying exceptional potential in fault prediction and diagnosis for complex systems. The paper highlights the significance of enhanced analytical techniques, such as cross-validation, ensuring the reliability and robustness of the model, contributing to refined and accurate fault prediction and diagnosis, all without succumbing to overfitting. This work significantly advances the application of machine learning in predicting malignancy within Industry 4.0, showcasing the promise of these methodologies in fault prediction and diagnosis for intricate systems. © 2024 IEEE.
Açıklama
3rd International Conference on Power, Control and Computing Technologies, ICPC2T 2024 -- 18 January 2024 through 20 January 2024 -- Raipur -- 198443
Anahtar Kelimeler
Fault-diagnosis, Feature Engineering, Machine learning, MetroPT, Predictive Maintenance
Kaynak
2024 3rd International Conference on Power, Control and Computing Technologies, ICPC2T 2024
WoS Q Değeri
Scopus Q Değeri
N/A
