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 project proposed is to develop a framework that connects sensor data with real-time processing to provide diagnostic/prognostic capabilities. Specifically, the proposed IIoT model is comprised of 3 components: the Cloud, the Fog and the Edge. The Cloud is used to store historic data as well as to perform demanding computations such as remaining useful life estimations. The Fog is the hardware that performs prognosis using information received both from sensing and the Cloud. The Edge is the bottom level hardware that filters data at the sensor level. In this investigation, an application of this method that uses multiple sensors to evaluate the state of health at laboratory conditions namely, acoustic emission, digital image correlation, and infrared thermography is presented. The key link that limits human intervention through data processing is the implemented database management approach. Specifically, a NoSQL database is implemented to provide live data transfer from the Edge to both the Fog and Cloud. In addition, the algorithms used capable to execute filtering followed by classification at the Fog level, as live data is recorded by the used sensors. The processed data is automatically sent to the Cloud for remaining useful life estimations and to perform forecasting.
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