High-Rate Structural Health Monitoring and Prognostics: An Overview

Dodson, Jacob; Downey, Austin; Laflamme, Simon; Todd, Michael D.; Moura, Adriane G.; Wang, Yang; Mao, Zhu; Avitabile, Peter; Blasch, Erik

doi:10.1007/978-3-030-76004-5_23

Cited by 16 publications

(8 citation statements)

References 18 publications

(17 reference statements)

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“…High-rate systems are defined as those experiencing dynamic events with amplitudes exceeding 100 g n over durations of less than 100 ms, such as blast mitigation systems, advanced weaponry, automotive airbag deployment mechanisms, and hypersonic vehicles. 3,4 The primary motivation behind conducting real-time state estimation for these systems lies in enabling feedback mechanisms to enhance operational performance and safety. Yet, this task is difficult, because these systems comprise large uncertainties in external loads, high levels of nonstationarity and heavy disturbances, and unmodeled dynamics resulting from changes in system configuration.…”

Section: Introductionmentioning

confidence: 99%

Real-time state estimation using recurrent neural network and topological data analysis

Razmarashooli,

Salazar Martinez,

Chua

et al. 2024

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVIII

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High-rate systems are defined as physical systems that undergo large perturbations, often exceeding 100 g's, over very short durations, often less than 100 milliseconds. Examples include blast mitigation mechanisms and advanced weaponry. The use of control feedback to empower high-rate systems requires the capability to estimate system states of interest in the realm of microseconds. However, due to the dynamics of these high-rate systems being highly nonlinear and nonstationary, it is challenging to predict their behavior using conventional state estimation methods. To address this issue, we conduct a study that explores the integration of topological data analysis (TDA) and recurrent neural network (RNN) to improve predictive capabilities for high-rate systems.Here, TDA features are used as the input to a machine learning algorithm to determine the state of a highrate system. We conduct practical evaluations using laboratory datasets from experiments in the dynamic reproduction of projectiles in ballistic environments for advanced research (DROPBEAR), focusing on localizing fast-changing boundary conditions on a cantilever beam. The study demonstrates the ability of the method to classify and predict a system's fundamental frequencies. This approach helps understand the structure of the underlying high-rate dynamics, leading to improved accuracy and precision in state estimation and prediction.

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Section: Introductionmentioning

confidence: 99%

Real-time state estimation using recurrent neural network and topological data analysis

Razmarashooli,

Salazar Martinez,

Chua

et al. 2024

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVIII

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“…For example, hypersonic, space, and military systems require active control within the microsecond (µs) timescale as dictated by the dynamics of the system. 1 Real-time modelbased control of these structures would enable real-time decision-making that would boost structure survivability in these extreme environments by modifying mission goals and outputs to changing conditions. According to Hong et al, 2 the high-rate problem is marked by: 1. significant external load uncertainty; 2. high levels of nonstationarities and heavy disturbances; and 3. produced dynamics from modifications to the system design.…”

Section: Introductionmentioning

confidence: 99%

“…5 When numerous time series components are present and their interactions need to be taken into account, a multivariate time arrangement is used. 6 The timing requirements driven by µs structural health monitoring were articulated by Dodson et al 1 Based on the dynamics of the considered "high-rate" class of systems, this work sets a system latency and forecasting horizon of 1 ms. To expand, the algorithmic work developed in this paper seeks to forecast the dynamic structural response (i.e. signal) 1 ms into the future while completing all required computations within a latency of 1 ms. To enable deterministic and low-latency time series forecasting of nonstationary signals, an FFT-based forecasting approach was developed that was implemented on a Field Programmable Gate Array (FPGA).…”

Section: Introductionmentioning

confidence: 99%

Hardware implementation of nonstationary structural dynamics forecasting

Chowdhury

Downey

Bakos

et al. 2023

Active and Passive Smart Structures and Integrated Systems XVII

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High-rate time series forecasting has applications in the domain of high-rate structural health monitoring and control. Hypersonic vehicles and space infrastructure are examples of structural systems that would benefit from time series forecasting on temporal data, including oscillations of control surfaces or structural response to an impact. This paper reports on the development of a software-hardware methodology for the deterministic and low-latency time series forecasting of structural vibrations. The proposed methodology is a software-hardware co-design of a fast Fourier transform (FFT) approach to time series forecasting. The FFT-based technique is implemented in a variable-length sequence configuration. The data is first de-trended, after which the time series data is translated to the frequency domain, and frequency, amplitude, and phase measurements are acquired. Next, a subset of frequency components is collected, translated back to the time domain, recombined, and the data's trend is recovered. Finally, the recombined signals are propagated into the future to the chosen forecasting horizon. The developed methodology achieves fully deterministic timing by being implemented on a Field Programmable Gate Array (FPGA). The developed methodology is experimentally validated on a Kintex-7 70T FPGA using structural vibration data obtained from a test structure with varying levels of nonstationarities. Results demonstrate that the system is capable of forecasting time series data 1 millisecond into the future. Four data acquisition sampling rates from 128 to 25600 S/s are investigated and compared. Results show that for the current hardware (Kintex-7 70T), only data sampled at 512 S/s is viable for real-time time series forecasting with a total system latency of 39.05 μs in restoring signal. In totality, this research showed that for the considered FFT-based time series algorithm the fine-tuning of hyperparameters for a specific sampling rate means that the usefulness of the algorithm is limited to a signal that does not shift considerably from the frequency information of the original signal. FPGA resource utilization, timing constraints of various aspects of the methodology, and the algorithm accuracy and limitations concerning different data are discussed.

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“…Examples of high-rate systems include adaptive airbag deployment systems, hypersonic vehicles, ballistic explosion events, and active blast mitigation mechanisms. The dynamics of these systems are uniquely defined by (1) large uncertainties in the external loads, (2) high levels of non-stationarity and heavy disturbances, and (3) unmodeled dynamics generated from changes in the system configurations, as described in an introductory paper [2].…”

Section: Introductionmentioning

confidence: 99%

Generated datasets from dynamic reproduction of projectiles in ballistic environments for advanced research (DROPBEAR) testbed

Laflamme

Moura

et al. 2022

IOPSciNotes

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High-rate dynamics occur when a system's acceleration is larger than 100 g$_\textrm{n}$ over durations less than 100~ms. Structural health monitoring (SHM) algorithms must be created for high-rate dynamic systems to maximize safety and minimize economic losses. There is a need to evaluate these SHM algorithms for precision and accuracy prior to real-world implementation. An experimental testbed was created to simulate large-magnitude events while maintaining repeatability to accurately and robustly assess various SHM algorithms' capability to monitor high-rate dynamic systems. All previous datasets created on the experimental testbed are discussed, examining various sensor setups, excitations, and boundary condition changes to properly simulate near-high-rate events and provide robust experimental data to evaluate SHM algorithms.

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High-Rate Structural Health Monitoring and Prognostics: An Overview

Cited by 16 publications

References 18 publications

Real-time state estimation using recurrent neural network and topological data analysis

Real-time state estimation using recurrent neural network and topological data analysis

Hardware implementation of nonstationary structural dynamics forecasting

Generated datasets from dynamic reproduction of projectiles in ballistic environments for advanced research (DROPBEAR) testbed

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