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Real-Time Machine Learning for High-Rate Structural Health Monitoring
Advances in science and engineering are empowering high-rate dynamic systems, such as hypersonic vehicles, advanced weaponries, and active shock and blast mitigation strategies. The real-time estimation of the structural health of high-rate systems, termed high-rate structural health monitoring (HRSHM), is critical in designing decision mechanisms that can ensure structural integrity and performance. However, this is a difficult task, because three aspects uniquely characterize these systems: (1) large uncertainties in the external loads; (2) high levels of non-stationarities and heavy disturbances; and (3) unmodeled dynamics generated from changes in system configurations. In addition, because these systems are experiencing events of high amplitudes (often beyond 100 g) over short durations (under 100 ms), a successful feedback mechanism is one that can operate under 1 ms. A solution to the unique system characteristics and temporal constraint is the design and application of real-time learning algorithms. Here, we review and discuss a real-time learning algorithm for HRSHM applications. In particular, after introducing the HRSHM challenge, we explore fast real-time learning for time series prediction using conventional and deep neural networks and discuss a path to rapid real-time state estimation.
Real-Time Machine Learning for High-Rate Structural Health Monitoring
Advances in science and engineering are empowering high-rate dynamic systems, such as hypersonic vehicles, advanced weaponries, and active shock and blast mitigation strategies. The real-time estimation of the structural health of high-rate systems, termed high-rate structural health monitoring (HRSHM), is critical in designing decision mechanisms that can ensure structural integrity and performance. However, this is a difficult task, because three aspects uniquely characterize these systems: (1) large uncertainties in the external loads; (2) high levels of non-stationarities and heavy disturbances; and (3) unmodeled dynamics generated from changes in system configurations. In addition, because these systems are experiencing events of high amplitudes (often beyond 100 g) over short durations (under 100 ms), a successful feedback mechanism is one that can operate under 1 ms. A solution to the unique system characteristics and temporal constraint is the design and application of real-time learning algorithms. Here, we review and discuss a real-time learning algorithm for HRSHM applications. In particular, after introducing the HRSHM challenge, we explore fast real-time learning for time series prediction using conventional and deep neural networks and discuss a path to rapid real-time state estimation.
Real-Time Machine Learning for High-Rate Structural Health Monitoring
Structural Integrity
Cury, Alexandre (editor) / Ribeiro, Diogo (editor) / Ubertini, Filippo (editor) / Todd, Michael D. (editor) / Laflamme, Simon (author) / Hu, Chao (author) / Dodson, Jacob (author)
Structural Health Monitoring Based on Data Science Techniques ; Chapter: 4 ; 75-94
Structural Integrity ; 21
2021-10-24
20 pages
Article/Chapter (Book)
Electronic Resource
English
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