Quantum-Enhanced Predictive Maintenance for Aerospace Robotic Arms

Abstract

The integration of quantum computing and neural networks has emerged as a transformative approach for addressing complex industrial challenges, particularly in predictive maintenance.This paper is the result of insights gained during the development of a Proof of Concept (PoC) in collaboration with Embraer, a leading aerospace company in Brazil, and SENAI UpLab, an innovation and technology hub of SENAI, one of Brazil’s foremost institutions for industrial education and applied research. Traditional predictive maintenance methods often struggle with the high-dimensional data and complex failure patterns inherent in aerospace systems. Leveraging the principles of quantum mechanics, QNNs and QSVCs offer enhanced computational power and the ability to process vast datasets exponentially faster than classical machine learning approaches. We propose a novel hybrid framework that combines QNNs and QSVCs, utilizing quantum superposition, entanglement, and kernel-based quantum algorithms to model the degradation patterns of robotic arm components. This enables early detection of potential failures and optimizes maintenance schedules with improved accuracy. The framework is validated using real-world data from aerospace robotic systems provided by EMBRAER, demonstrating superior performance in terms of accuracy, efficiency, and robustness compared to classical machine learning methods. Our results, supported by the collaboration with SENAI UpLab, highlight the potential of quantum-enhanced techniques, including QNNs and QSVCs, to revolutionize predictive maintenance in the aerospace industry, reducing downtime, minimizing costs, and enhancing operational safety. This study contributes to the growing body of research at the intersection of quantum computing and industrial applications, paving the way for future advancements in intelligent maintenance systems.