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Smart systems for fault diagnosis and monitoring of industrial assets based on the industry 4.0 concept

Abstract

This research project aims to develop advanced methodologies for diagnosing and classifyingfailures in aerospace composite structures, aligning with the current trend in the industry and the concept of "Industry 4.0." The proposed approach will utilize data obtained from acoustic emission sensors, advanced feature extraction techniques, and pattern recognition methods to validate the methodologies. The validation will be performed by examining delamination and microcrack phenomena in carbon fiber-reinforced polymer (CFRP) structures and designing condition-based indicators through state-of-the-art deep learning algorithms and convolutional neural networks (CNNs). The research activities will concentrate on industrial automation to optimize and reduce manufacturing costs through the early identification of various types ofdamage and the prediction of the remaining useful life (RUL) of aerospace composite structures. This project will also guide scientific initiation, master's, and/or doctoral research work under the supervision of the principal investigator. The expected outcomes will contribute to the hostinstitution by generating new knowledge and disseminating it through publications in reputable journals. Additionally, it may lead to the formation of potential collaborations between academia and industry and the possibility of new research projects. (AU)

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