Aerospace (May 2020)
The Industry Internet of Things (IIoT) as a Methodology for Autonomous Diagnostics in Aerospace Structural Health Monitoring
Abstract
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.
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