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Full-Text Articles in Maintenance Technology
The Evolution Of Mro Outsourcing: Global Trends, Regulatory Challenges, And Strategic Considerations In Aviation Maintenance, David C. Ison
The Evolution Of Mro Outsourcing: Global Trends, Regulatory Challenges, And Strategic Considerations In Aviation Maintenance, David C. Ison
Journal of Aviation Technology and Engineering
The maintenance, repair, and overhaul (MRO) industry plays a critical role in ensuring the safety, efficiency, and sustainability of the global aviation sector. As airlines seek to reduce costs, optimize operations, and access specialized expertise, MRO outsourcing has become an increasingly prevalent strategy. This essay examines the key drivers of MRO outsourcing, including cost reduction, access to advanced technologies, and the focus on core competencies. The essay also explores emerging trends such as the rise of new MRO hubs in Asia and the Middle East, the integration of predictive maintenance and artificial intelligence, and the growing emphasis on environmental sustainability. …
The E-Strip® As A Tool For Predictive Maintenance To Monitor And Control Shot Peening Processes, Walter A. Beach, Scott Glasier
The E-Strip® As A Tool For Predictive Maintenance To Monitor And Control Shot Peening Processes, Walter A. Beach, Scott Glasier
15th International Conference on Shot Peening
The primary objective of this paper is to demonstrate the E-Strip® is capable of providing real-time feedback to the machine controller for making necessary process adjustments due to external forces, such as machine wear. Adjustments may include regulating air pressure and shot flow within pre-defined tolerances dictated by specification. It is anticipated that this integrated approach will significantly reduce variations in intensity levels observed from part to part and lot to lot.
Predictive Maintenance Analysis Of Turbofan Engine Sensor Data, Stanley A. Melkumian
Predictive Maintenance Analysis Of Turbofan Engine Sensor Data, Stanley A. Melkumian
The Journal of Purdue Undergraduate Research
Predictive maintenance in aviation and aerospace applications is among the most explored problems in machine learning (ML) and artificial intelligence (AI), and datasets such as NASA’s C-MAPPS turbofan engine degradation simulation data have proven invaluable, helping researchers explore numerous questions on engine performance, maintenance, and failure. The purpose of this study was to extend the current research on predicting the remaining useful life (RUL) of engines and their risk classification. Starting with simple yet under-investigated nonlinear survival and random forest models, the analysis implemented eXtreme Gradient Boosting (XGBoost) and long short-term memory (LSTM) from TensorFlow’s Keras library. For both regression …