Improved denoising methods for smoothing frequency response functions

Abstract

Measurement errors, including but not limited to noise, persist regardless of the sophistication of the experimental measurement system. Measurements contaminated with noise have proved challenging to work with from a post-processing standpoint, especially when the measurements are used directly in the analytical processing. Noise contamination in frequency response functions limits the accuracy of many applications, such as impedance modeling, requiring the direct use of measured frequency response function data. Existing approaches, such as Modal Parameter Estimation (curve-fitting), that address the de-noising of frequency response functions also suffer from various deficiencies. The errors associated with the estimation process, including modal truncation along with user interaction and interpretation, are often challenging to mitigate.Two alternative methods of frequency response function de-noising were developed in this work. The first method involves a direct approach that smooths measured frequency response functions without Modal Parameter Estimation or significant user interaction. The second approach works to alleviate the truncated effects of unmeasured modes in frequency response function synthesis more effectively than the existing upper and lower residual compensation terms. The proposed frequency response function de-noising methods are validated analytically and experimentally using various component finite element models and experimental tests.