Sep 2026· Journal of Mechanical Science and Technology· Vol 40, pp. 6923 - 6936· 0 citations· 39 references
TL;DR
Results indicate that Fuel_consumption, Running_period, and Engine_load are the best trio of features that influence prediction accuracy (A) in all hybrid models, which confirms that energy consumption and operational stress are the main factors in early fault identification.
This study proposes a SCADA-integrated predictive maintenance framework for aircraft engine health monitoring by leveraging machine learning and deep learning models. The objective is to enhance early fault detection capability, reduce unexpected failures, and support cost-efficient maintenance strategies within av...
U. Himmet, Emre Kiyak· Aircraft Engineering and Aer...· 0 citations
Aiming at the challenge of fault evolution prediction and intelligent diagnosis in power equipment operation and maintenance, this paper proposes a joint model driven by multi-source operation data. By fusing heterogeneous data such as electric quantity, oil chromatography and temperature, an improved health index (HI)...
Zi-Yi Ding, Ren-Jie Qin, Shen Han et al.· International Conference on...· 0 citations
The industrial machinery provides constant stream of sensor readings, which can be effectively used for failure prediction and maintenance decisions. This research work proposes a novel approach to enable predictive maintenance and energy saving through the use of AI4I 2020 Predictive Maintenance Dataset. In the propos...
Aaron S., A. S, Balavijay M. et al.· Journal of Electrical Engine...· 0 citations
This study concludes that the integration of structural mechanics principles with data-driven AI models—particularly through physics-informed neural networks and edge-deployed lightweight models—represents the most promising direction for next-generation onboard intelligent health monitoring systems.
Yizhe Lyu· Applied and Computational En...· 0 citations
An integrated explainable artificial intelligence (XAI) framework for predictive
maintenance is proposed, focusing on failure prediction, Remaining Useful Life (RUL)
estimation, and real-time asset monitoring. The framework is evaluated using a
synthetically generated predictive maintenance data simulating 50 machines...
Milica Tufegdžić· SMART MANUFACTURING ON THE H...· 0 citations
Deployment simulations demonstrate that the AI-PdM framework generalizes with greater than 90% accuracy, reduces unplanned downtime by approximately 60% (range 50-70%), and lowers overall maintenance cost by approximately 35% (range 25-40%) relative to reactive and preventive strategies.
