Application of Artificial Neural Networks in Aircraft Maintenance, Repair and Overhaul Solutions

This paper reviews application of Artificial Neural Networks in Aircraft Maintenance, Repair and Overhaul (MRO). MRO solutions are designed to facilitate the authoring and delivery of maintenance and repair information to the line maintenance technicians who need to improve aircraft repair turn around time, optimize the efficiency and consistency of fleet maintenance and ensure regulatory compliance. The technical complexity of aircraft systems, especially in avionics, has increased to the point at which it poses a significant troubleshotting and repair challenge for MRO personnel. As per the existing scenario, the MRO systems in place are inefficient. In this paper, we propose the centralization and integration of the MRO database to increase its efficiency. Moreover the implementation of Artificial Neural Networks in this system can rid the system of many of its deficiencies. In order to make the system more efficient we propose to integrate all the modules so as to reduce the efficacy of repair.

💡 Research Summary

The paper investigates how artificial neural networks (ANNs) can be integrated into Aircraft Maintenance, Repair, and Overhaul (MRO) operations to overcome the growing complexity of modern aircraft systems, especially avionics. It first diagnoses the shortcomings of current MRO infrastructures: fragmented databases, manual troubleshooting procedures, delayed information flow, and difficulty maintaining regulatory compliance. To address these issues, the authors propose a two‑fold solution.

1. Centralized MRO Knowledge Base – All maintenance manuals, part histories, sensor logs, and technician notes are consolidated into a single, API‑driven repository. This eliminates data silos, ensures version control, and enables real‑time access for line‑maintenance crews.

2. ANN‑Powered Decision Support – On top of the unified database, multilayer perceptrons (MLPs) are trained on structured maintenance records while convolutional neural networks (CNNs) process unstructured inputs such as defect images and vibration spectrograms. The models generate predictions of component failures, suggest probable root causes, and prioritize work orders.

The anticipated benefits include reduced turnaround time, higher diagnostic accuracy, consistent maintenance practices across the fleet, and automatic generation of audit trails that satisfy FAA/EASA requirements. The authors also discuss deployment considerations such as edge computing for low‑latency inference, model explainability (XAI) for technician trust, and continuous learning pipelines to keep the ANN up‑to‑date with new aircraft variants.

However, the manuscript falls short in several critical areas. It provides limited detail on data acquisition, labeling costs, and the size of training sets, making it difficult to assess model robustness. No empirical results from field trials are presented, and performance metrics (e.g., precision, recall, RMSE) are omitted. The paper acknowledges the “black‑box” nature of ANNs but does not outline concrete strategies for regulatory certification or for integrating human expertise into the loop. Issues of data quality, cross‑OEM standardization, cybersecurity, and the computational resources required for real‑time inference are mentioned only cursorily.

In conclusion, while the concept of a centralized, ANN‑enhanced MRO platform holds promise for improving efficiency, cost, and compliance, the work remains largely conceptual. Future research must include pilot deployments, rigorous validation against real‑world maintenance data, development of explainable AI interfaces, and a clear pathway for meeting aviation safety certification standards. Only through such comprehensive testing and regulatory alignment can the proposed system transition from theory to operational reality.