High-rate dynamic systems are defined as systems that undergo large levels of acceleration, often over 100g, over short durations, typically less than 100 ms. Examples of such systems include active blast mitigation mechanisms, adaptive air bag deployment, and hypersonic systems. Their dynamics is uniquely characterized by 1) large uncertainties in the external loads; 2) high levels of nonstationarities and heavy disturbances; and 3) unmodeled dynamics generated from changes in system configurations. High-rate structural health monitoring (HRSHM) is concerned with the development of sub-millisecond state estimation capabilities in order to facilitate the future implementation of decision systems to improve the safety and operation of high-rate systems.
Many structural systems, such as aircraft, orbital infrastructure, and energy harvesting devices, experience dynamic forces along with changing structural boundary conditions. Collecting and analyzing data on these systems provides useful insight that aids design, evaluation, and function. For real-time decision-making on systems experiencing high-rate changes, completing assessments quickly enough to be relevant poses a unique set of challenges. In systems sufficiently understood and well defined, determining a system's state that experiences high-rate structural boundary condition changes can be accomplished by monitoring its frequency response. In this work, methods of frequency detection applicable to real-time state estimation of structures experiencing high-rate boundary changes were investigated; progress and findings in extracting the frequency response of a structure in real-time are presented here. A novel Delayed Comparison Error Minimization technique is presented and experimentally validated using the DROPBEAR experimental testbed at the Air Force Research Laboratory. This testbench consists of an oscillating beam with one end fixed and roller support that can move along the beam's length. Real-time estimation of pin location through the measurement of beam motion was performed using the novel Delayed Comparison Error Minimization technique. Results are compared against an FFT-based method with a variety of window lengths. The latency and precision of this method are analyzed, and the results from each method are compared, with a discussion on the applicability of each method.
High-rate dynamic systems undergo events of amplitudes greater than 100 gs in a span of less than 100 ms. The unique characteristics of high-rate dynamic systems include 1) large uncertainties in the external loads, 2) high levels of non-stationarity and heavy disturbances, and 3) unmolded dynamics generated from changes in the system configurations. This paper presents a deep learning algorithm consisting of an ensemble of long short-term memory (LSTM) cells used to conduct high-rate state estimation. The ensemble of LSTMs receives and transforms the signal into inputs of different time resolutions. Each input vector correlates to an LSTM cell which predicts the signal in real-time and produces feature vectors. The feature vectors are then processed through an attention layer and dense layer to predict the physical features of the system. Here, we study the temporal evolution of the attention layer weights to conduct state estimation, while the LSTM cells are attempting to conduct measurement predictions. We study the performance of the algorithm on experimental data generated by DROPBEAR, a dedicated testbed for high-rate structural health monitoring research. State estimation consists of estimating, in real-time, the location of a cart that moves along a beam. Results show that the attention layer weights can be used to estimate the cart location but that the beam requires impact excitations to accelerate the convergence of the algorithm.
High-rate systems operating in the 10 μs to 10 ms timescale are likely to experience damaging effects due to rapid environmental changes (e.g., turbulence, ballistic impact). Some of these systems could benefit from real-time state estimation to enable their full potential. Examples of such systems include blast mitigation strategies, automotive airbag technologies, and hypersonic vehicles. Particular challenges in high-rate state estimation include: 1) complex time varying nonlinearities of system (e.g. noise, uncertainty, and disturbance); 2) rapid environmental changes; 3) requirement of high convergence rate. Here, we propose using a Variable Input Observer (VIO) concept to vary the input space as the event unfolds. When systems experience high-rate dynamics, rapid changes in the system occur. To investigate the VIO’s potential, a VIO-based neuro-observer is constructed and studied using experimental data collected from a laboratory impact test. Results demonstrate that the input space is unique to different impact conditions, and that adjusting the input space throughout the dynamic event produces better estimations than using a traditional fixed input space strategy.
Recent advances in Structural Health Monitoring have provided the means of eliminating the prerecorded baseline
measurement by producing an instantaneous baseline. The damage detection method presented is near-real time damage
detection instantaneous baseline method by using ambient excitation and passive sensing. The method uses an array of
sensors and compares the features in the data of the different wave-propagation paths to determine an undamaged
baseline. The wave-propagation paths that greatly differ from the instantaneous baseline indicate the location of damage
along those paths. The details of the signal processing algorithm and evaluation of the method for detecting damage are
included. The damage detection method presented is able to detect damage in a wave path using an instantaneous base line.
Early results and status of a research effort to frame the possibility in compressing the time scale of structural health
monitoring to the impulsive transient domain are presented. Output only modal methods using a frequency domain
decomposition technique are used to extract the operational modes of a plate subject to impulsive loading. A strain
energy method for plates is the used to detect the damage on the plate. The method detects damage, but the location of
damages is not very precise. The development of an extremely short duration, transient structural health monitoring
algorithm will be discussed. Challenges in studying this new domain of health monitoring will also be highlighted.
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