KEYWORDS: Field programmable gate arrays, Clocks, Neurons, Signal to noise ratio, Structural health monitoring, Sensors, Detection and tracking algorithms, Data acquisition, Damage detection, Algorithm development
Hard real-time time-series forecasting of temporal signals has applications in the field of structural health monitoring and control. Particularly for structures experiencing high-rate dynamics, examples of such structures include hypersonic vehicles and space infrastructure. This work reports on the development of a coupled softwarehardware algorithm for deterministic and low-latency online time-series forecasting of structural vibrations that is capable of learning over nonstationary events and adjusting its forecasted signal following an event. The proposed algorithm uses an ensemble of multi-layer perceptrons trained offline on experimental and simulated data relevant to the structure. A dynamic attention layer is then used to selectively scale the outputs of the individual models to obtain a unified forecasted signal over the considered prediction horizon. The scalar values of the dynamic attention layer are continuously updated by quantifying the error between the signal’s measured value and its previously predicted value. Deterministic timing of the proposed algorithm is achieved through its deployment on a field programmable gate array. The performance of the proposed algorithm is validated on experimental data taken on a test structure. Results demonstrate that a total system latency of 25.76 µs can be achieved on a Kintex-7 70T FPGA with sufficient accuracy for the considered system.
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.
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.
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