Combining acoustic emission and guided ultrasonic waves for global property prediction and structural health monitoring of glass fiber composites
Since the turn of the century, wind turbines have been rapidly growing in size and are projected to continue growing as the technology develops. These increases in size have led to increased failure rates of the glass fiber composite turbine blades. Because of this, it is of utmost importance to understand failure mechanisms in glass fiber composites and investigate new approaches to predicting failures. This has led to advancements in structural health monitoring of large composites structures by applying sophisticated sensing technologies, in attempts to evaluate material damage states and predict structural failures before they occur. This research has taken a novel approach to apply multiple ultrasonic monitoring techniques, in the form of acoustic emission and guided ultrasonic waves, simultaneously to the mechanical testing of glass fiber reinforced composite laminates. Testing of the composite laminates was conducted in the form of increasing load-unload-reload static tension tests and tension-tension fatigue tests, to measure modulus degradation of the laminates while applying the monitoring techniques. Acoustic emission was used to detect damage events that occurred within laminates in real-time and guided ultrasonic waves were applied periodically to the laminates to observe changes in wave propagation and relate back to damage severity within the laminates. Furthermore, the acoustic emission and guided ultrasonic wave datasets were combined and used to train multivariate regression models to predict modulus degradation of the laminates tested, with no prior knowledge of the laminates' loading histories. Overall, the predictive models were able to make good predictions and showed the potential for combining multiple monitoring techniques into singular systems and statistical predictive models. This research has shown that the combination of the two measurement techniques can be implemented for more accurate and reliable monitoring of large composite structures than the techniques used individually, with minimal additional hardware. Ultimately, this research has paved the way for a new form of smart structural health monitoring, with superior predictive capabilities, which will benefit the renewable energy through reducing maintenance and repair costs and mitigating the risk of wind turbine blade failures.