Towards Future Intelligent Structural Systems via Bio-inspired State-sensing and Awareness

Future intelligent mechanical, aerospace and civil structural systems will be able to “feel”, “think”, and “react” in real time based on high-resolution state-sensing, awareness, and self-diagnostic capabilities. They will be able to sense and observe phenomena at unprecedented length and time scales allowing for superior performance and adaptability, increased safety and resilience, optimal control, reduced operational and maintenance costs, and complete life-cycle management. Towards this end, current research efforts aim at the development of novel technologies that will lead to the next generation of intelligent self-sensing self-diagnostic structural systems that can sense the surrounding environment, interpret the sensing information in real time, determine their actual operating state and health condition in complex dynamic environments, and make optimal decisions for control and mitigation in the face of uncertainty.

In an effort to address these goals in a unified way, this talk will present a novel framework for the development of intelligent self-sensing self-diagnostic structural systems inspired by the unprecedented sensing and awareness capabilities of biological systems. This framework incorporates (i) bio-inspired distributed multi-modal micro-sensor networks, (ii) data-driven methods for the global modeling and identification of structural systems under varying operating states and uncertainty, and (iii) integrated state awareness and structural health monitoring (SHM) approaches for inferring the actual operating and structural health state. Prototype intelligent systems with embedded sensing and awareness capabilities will be presented with special emphasis placed on the novel concept of “fly-by-feel” aerial vehicles. Distributed micro-sensors in the form of stretchable sensor networks are used to provide the sensing capabilities, while stochastic system identification, statistical signal processing, machine learning and SHM diagnostic techniques are employed for the accurate interpretation of the sensing data and subsequent determination the operating state and structural health condition. The ultimate goal of this presentation is to provide a concise overview of the main research developments that constitute a “conceptual leap” to overcome the current limitations towards the development of the next generation of intelligent structural systems that can “feel”, “think”, and “react”.

Bio:
Dr. Fotis Kopsaftopoulos is a Postdoctoral Research Fellow in the Structures and Composites Laboratory in the Department of Aeronautics and Astronautics at Stanford University. He received his Diploma and Ph.D. in Mechanical and Aeronautical Engineering from University of Patras, Greece, on the topic of advanced functional and sequential time series methods for vibration-based structural health monitoring (SHM). His background and interests span the areas of intelligent structural systems with embedded sensing and awareness capabilities, structural health monitoring (SHM), stochastic modeling and identification, bio-inspired fly-by-feel aerial vehicles, and integration of data-based with multi-scale physics-based methods for “smart” data analysis. Dr. Kopsaftopoulos has participated in various national, international and industrially supported research projects both in the U.S.A. (AFOSR, NASA, NSF, ARPA-e, Boeing) and Europe (FP6 and FP7, Airbus, RUAG Space). He is a member of the Organizing Committee of the International Workshop on Structural Health Monitoring (IWSHM) and serves as an Associate Editor of the Structural Health Monitoring international journal.