AN AUTONOMOUS EARLY WARNING SYSTEM CONCEPT FOR REAL-TIME REMOTE MONITORING OF CRITICAL STRUCTURES

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Over the years, Structural Health Monitoring (SHM) and related concepts that focus on monitoring & managing the health of critical structures have gained vast popularity, especially considering the rapid growth and implementation of industry 4.0 approaches in the industrial manufacturing, production and operational environments. However, the implementation of SHM, which is clearly defined by the industry pain-points, have not risen on-par with the expectations of the end users. We believe that this gap could be associated with the following key points, a. the challenges in collecting, handling and analyzing the data collected from the sensors used for monitoring the health of these structures; b. the lack of SHM implementation concepts that establish an added value in rapid decision making and c. the lack of clear V&V approaches that establishes the technological maturity. Although the currently available state-of-the-art sensors are capable of collecting and communicating the sensor data, the complexity in data handling on the edge in combination with selection of the appropriate communication protocol hinders its value for industrial implementation. In addition, in a lot of cases, we observe that the available solutions for remote sensor connectivity do not correspond to the power and connectivity requirements of the end-user. This establishes the need for revisiting the implementation concept of SHM in the upcoming digital future. In this paper, we propose an autonomous early warning system based on smart sensors and the Internet of Things (IoT) for real-time remote monitoring of critical structures. We address the challenges associated with collecting, handling, and analyzing data from sensors used for structural health monitoring (SHM) and highlight the need for a digital, real-time, and reliable sensor solution. Our approach uses novel intelligent sensors, developed and provided by IPR as part of our joint collaborative effort, with edge-computing and an efficient and low data rate (due to data pre-processing and compression into meaningful “digests") wireless communication infrastructure for data collection and new methodologies for data integration & data analysis. We elaborate on the relevant pain-points faced by end-users across several industries, including infrastructure, aerospace, railway, and marine. We describe our autonomous early warning system concept, focusing on its technical capabilities and how it compares with the needs and requirements of end-users. We also discuss our V&V approach, which focuses on the added value of cross-industry innovation and aims to generate innovative solutions and new business cases for SHM across industries. Overall, we believe that our pain-point first approach and our proposed concept can generate vast amounts of experiences and data necessary to establish SHM as a fundamental and necessary concept for estimating the structural integrity of criticsl sltructures. By leveraging smart sensors and the IoT, we can provide real-time monitoring and early warning systems that can help prevent catastrophic failures and improve the overall safety and reliability of critical infrastructure.