A novel structural health monitoring approach consisting of guided ultrasonic waves, acoustic emission, and digital image correlation, as well as real-time and postmortem analyses, was implemented to monitor and quantify crack growth in Al 2024 compact tension specimens, designed, and precracked according to ASTM E647-08. Tensile loads were applied according to ASTM E1290-08. Guided ultrasonic waves were generated with pulses centered at three different frequencies and were recorded using piezoelectric transducers. Guided ultrasonic waves were also modeled using finite element wave propagation models. The same transducers were further used for online acoustic emission monitoring. A digital image correlation system continuously monitored the crack growth and provided full-field surface strains. The application of this integrated structural health monitoring approach resulted in reliable damage detection and quantified crack growth measurements. In addition, a novelty detector based on the Mahalanobis distance was implemented in a data fusion scheme to assess the extent of damage. The reported results constitute a proof-of-concept investigation of a novel structural health monitoring approach based on the combination of real-time optical and acoustic nondestructive testing.

The predominant method for infrastructure evaluation is visual inspection. The large number and scale of inspections often make it difficult to catalog the location, size, and severity of the identified damage. Several nondestructive methods can be used to detect cracks, such as acoustic emission and ultrasound, but they require the installation of many sensors, typically involve measurements in a predetermined area, and often require post-processing. Though image-based crack detection is limited to inspecting surface cracks, it has advantages including speed, repeatability, and large area coverage. Furthermore, the use of image stitching can provide a comprehensive assessment of the health of the entire structure. This method could also be applied using an unmanned aerial vehicle (UAV). This paper discusses major challenges in automated crack detection and implementation on UAVs.

Structural Health Monitoring (SHM), defined as the process that involves sensing, computing, and decision making to assess the integrity of infrastructure, has been plagued by data management challenges. The Industrial Internet of Things (IIoT), a subset of Internet of Things (IoT), provides a way to decisively address SHM’s big data problem and provide a framework for autonomous processing. The key focus of IIoT is operational efficiency and cost optimization. The purpose, therefore, of the IIoT approach in this investigation is to develop a framework that connects nondestructive evaluation sensor data with real-time processing algorithms on an IoT hardware/software system to provide diagnostic capabilities for efficient data processing related to SHM. Specifically, the proposed IIoT approach is comprised of three components: the Cloud, the Fog, and the Edge. The Cloud is used to store historical data as well as to perform demanding computations such as off-line machine learning. The Fog is the hardware that performs real-time diagnostics using information received both from sensing and the Cloud. The Edge is the bottom level hardware that records data at the sensor level. In this investigation, an application of this approach to evaluate the state of health of an aerospace grade composite material at laboratory conditions is presented. The key link that limits human intervention in data processing is the implemented database management approach which is the particular focus of this manuscript. Specifically, a NoSQL database is implemented to provide live data transfer from the Edge to both the Fog and Cloud. Through this database, the algorithms used are capable to execute filtering by classification at the Fog level, as live data is recorded. The processed data is automatically sent to the Cloud for further operations such as visualization. The system integration with three layers provides an opportunity to create a paradigm for intelligent real-time data quality management.