Date of Award
Doctor of Philosophy
Civil, Construction, and Environmental Engineering
Over the past few decades, Structural Health Monitoring (SHM) has gained wide popularity as it became essential to integrate monitoring systems into complex structural systems to ensure structural integrity and minimize hazards that may arise from any structural failure or collapse. Current monitoring technologies are capable of monitoring small areas with a priori information about the location of an existing defect, therefore they lack the ability to efficiently monitor large-scale systems such as bridges and wind turbine blades. The objective is to create a system capable of performing real-time online SHM with continuous feedback. This research aims at developing a complete system for structural health monitoring of wind turbine blades, including the sensing element, the data acquisition system, and the damage detection algorithm. The proposal is a network of soft sensors covering large surfaces capable of monitoring global as well as local behavior. The advantages of such a solution include cost efficiency, customizability in size and shape to accommodate the application, simple fabrication and installment and direct feature extraction with simple signal processing and machine learning techniques. The studies conducted to complete this dissertation intended to develop the sensing element (material and fabrication), characterize it and demonstrate applications to real structures. The proposed sensing element is a novel soft elastomeric capacitor (SEC) sensor for monitoring of large surfaces, applicable to composite materials. This soft capacitor is fabricated using a highly sensitive elastomer sandwiched between electrodes. It transduces strain into changes in capacitance. The elastomer is made of a Styrene Ethylene Butylene Styrene (SEBS) polymer doped with high permittivity Titanium dioxide (TiO2) as a filler material to increase the overall composite permittivity and improve the durability. The electrodes are made of a similar polymer doped with carbon black particles.
The first study was conducted on optimizing the fabrication process for the sensor. We investigated the influence of processing methods that dictate the performance enhancement in a nanocomposite soft capacitor. The efficiency of ultrasonic probe and high-shear melt mixing methods in dispersing TiO2 nanoparticles in SEBS polymer matrix was studied. The compression-molding method shows highly promising for engineering applications by enhancing fabrication speed, safety, and improving control over the film thickness.
The second part investigated the influence of interfacial treatment on the matrix-filler interaction using a melt-mixing process to fabricate robust and highly stretchable elastomers. Silicone oil and silane coupling agent were studied as possible solutions to enhance the compatibility between the inorganic fillers and polymer matrix. Results showed that specimens filled with silicone oil coated particles have promising overall properties.
The third part, consisted of several experiments to characterize the functionality and applicability of the SEC to implement SHM to real life structures. The sensor behavior under static and dynamic loads was evaluated. Static test results showed the capability of the sensor to measure strains above 25µε with an almost linear behavior up to 20% strain levels and a gauge factor of 2. Dynamic results showed capability to accurately detect frequency contents and mode shapes. All the characterization tests were verified with one or more method, including commercial strain gauges, accelerometers, and finite element models.
Using SECs in a network configuration have a great potential to implement an efficient inexpensive real time SHM on large-scale structures such as wind turbine blades. SEC data can be used to perform damage detection, localization and prognosis based on statistical as well as vibration analysis.
Hussam Suhail Saleem
Saleem, Hussam Suhail, "Highly scalable bio-inspired soft elastomeric capacitor for structural health monitoring applications" (2015). Graduate Theses and Dissertations. 14921.