Holistic Predictive Maintenance for CFRPs under Multi-modal Loading Using Electromechanical Characterization

Abstract

This study aims to develop a holistic predictive approach for carbon-fiber-reinforced polymers (CFRPs). This includes real-time structural health monitoring (SHM) and prognostics and health management (PHM) measures for securing structural integrity and saving maintenance expenses. SHM involves the real-time identification of current health status of the material, and PHM enables prediction of health indicators and remaining useful life (RUL). Based on the idea that the conductive network inherent in CFRPs can be modeled as electrical circuits, structural deformation and damages can be analyzed by means of electrical resistance of the material, which can be monitored in real-time. By integrating the signal simultaneously measured under multi-modal loadings with optimal data processing approaches, the health status of CFRPs have been characterized. The keynotes of the thesis are shown below:

1. Demonstrate quantitative relationships between CFRP damage and electromechanical responses 2. Establish general decision-making SHM/PHM algorithms for enhanced performance and output practicality 3. Predict RUL of CFRP structures with a high level of reliability

Starting from the basic electrical circuit model, damages in CFRPs were quantitatively analyzed. The electromechanically represented damages were digitized and presented as health indices, which can estimate the degradation in mechanical properties. Existing self-sensing studies only exploit the rough relationship between electrical resistance and mechanical strain which results in poor reproducibility and confined to abstract damage information. To address these intrinsic limitations, machine learning approaches were involved to extract detailed outcomes, such as location, type, and severity of random damages. The algorithm has also been extended into 3-dimensional structures under multi-modal loading scenarios targeting industrial in-service applications. One of the most crucial parts to complete predictive maintenance is reliable RUL prediction. A hybrid algorithm was newly proposed by integrating statistics-based and machine-learning approaches to overcome existing time-consuming and knowledge-dependent tasks. In addition, a material degradation model which can precisely explain electromechanical responses during fatigue fracture was suggested. The ultimate goal of this dissertation is ‘extracting maximal information from minimal signal.’ Towards efficient operation and economic maintenance of CFRP structures, electromechanical characterization with a tight collaboration with mechanical analyses has been performed throughout the study.