Applications of Machine Learning in Aircraft Maintenance
Journal Title: Journal of Engineering Management and Systems Engineering - Year 2023, Vol 2, Issue 1
Abstract
Aircraft maintenance is an expansive multidisciplinary field which entails robust design and optimization of extensive maintenance operations and procedures; encompassing the fault identification, detection and rectification, and overhauling, repair or modification of aircraft systems, subsystems, and components, as well as the scheduling for various maintenance operations, in compliance with the aviation standards; in order to predict, pre-empt and prevent failures and thus ensure the continual reliability of aircraft. Advances in Big Data Analytics (BDA) and artificial intelligence techniques have revolutionized predictive maintenance operations. Predictive maintenance is making big strides in the aerospace sector accompanied by a variety of prognostic health management options. Artificial intelligence algorithms have recently been extensively applied to optimize aircraft maintenance systems and operations. Several researchers have proposed, analysed, and investigated the applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) based data analytics for predictive maintenance of aircraft systems, subsystems, and components. This paper provides a comprehensive review of the ML techniques like Multilayer Perceptron (MLP), Logic Regression (LR), Random Forest (RF), Artificial Neural Network (ANN), Support Vector Regression (SVR), Linear Regression (LR), and other common ML techniques for their present implementation and potential future applications in aircraft maintenance.
Authors and Affiliations
Umur Karaoğlu, Osinachi Mbah, Qasim Zeeshan
Keywords
Related Articles
- EP ID EP731819
- DOI 10.56578/jemse020105
- Views 71
- Downloads 0
Modeling of Mexican Hat Wavelet Neural Network with L-BFGS Algorithm for Simulating the Recycling Procedure of Waste Plastic in Ocean
In the global economy, plastics are considered a versatile and ubiquitous material. It can reach to marine ecosystems through diverse channels, such as road runoff, wastewater pathways, and improper waste management. The...
Risk Management in the Transport of Dangerous Goods in Hungary: A Statistical and FMEA-Based Case Study on Bitumen Transportation
Risk management in the transportation of dangerous goods is critical for safeguarding human health, the environment, and infrastructure. This study explores systematic methodologies for risk assessment in the context of...
Evaluating Performance-Based Logistics in Manufacturing Through Polytopic Fuzzy SWARA: A Criterion Assessment Approach
Effective management of supply chains, pivotal for sustaining business operations, is increasingly challenged by rising costs and complexity in logistics processes. Performance-Based Logistics (PBL) emerges as a critical...
Optimising O2O Supply Chain Strategies Through Cost-Sharing Contracts: Strategic Analysis and Empirical Insights
Manufacturers are increasingly leveraging both online and offline channels to diversify their sales strategies. However, competition between these channels presents challenges in maximising profits for all parties involv...
A Review of Digital Twin in Logistics: Applications and Future Works
The logistics industry faces many challenges, such as low efficiency and transparency, and data cannot be updated in real time. Digital twin in logistics is regarded as a new technology that can lead the further developm...