DOE OSTI2018
Mechanical devices operating in an environment contaminated by noise, uncertainties, and extraneous disturbances lead to low signal-to-noise-ratios creating an extremely challenging signal processing problem—especially for on-line, realtime, in-flight application. In this report, we discuss a model-based approach to incorporate physical device features into a dynamic structure that can be used to perform on-line tracking of modal frequencies. The approach we take, after pre-processing the raw measurement data, is to obtain a representation of the structural device under test that captures its underlying response to a suite of excitations and operating conditions. One approach is to apply modern system identification techniques based on stochastic subspace methods capable of both: (1) identifying the underlying black-box structure enabling the extraction of structural modes that can be used for analysis; and (2) modal tracking under continued vibrational excitation as in a real-time, in-flight like environment. The vibrational signature of the structural device is measured directly using a suite of tri-axial accelerometers characterizing a multiple input/multiple output (MIMO) dynamic system. In this analysis, we have 15 tri-axial accelerometer measurements or 45 total output channels along with a single excitation also measured with a tri-axial accelerometer providing a 3-input system for processing. We initially describe the experiment and then briefly develop the necessary background in stochastic subspace realization, discuss the algorithm and apply it to the isolated structural device, first, characterized by the set of noisy MIMO accelerometer measurements extracting the modal model from the data (subspace identification), extract the unique modal frequencies (modal coordinate transformation), track their evolution (real-time tracker) to evaluate the overall performance of the processor. Next we evaluate processor performance when the structural device is incorporated into a flight vehicle for further performance analysis. We summarize the results and predict performance.