Identification of non-conforming temporal patterns in mixed shock and vibration data with the Information Impulse Function

Abstract

The Information Impulse Function (IIF) is an analysis technique that operates on time–frequency representations of a signal to detect non-conforming, transient patterns. Transient patterns can signify events of scientific and engineering interest but may be difficult to confidently identify against non-Gaussian backgrounds. IIF analysis highlights non-conforming patterns and diminishes conforming patterns, providing a high confidence discriminator between potential events of interest and the background. This is accomplished by first calculating the relative participation of the temporal principal shape vectors as a function of signal compression. The integral of the resulting surface represents both signal complexity and uniqueness per instance in time. To demonstrate the IIF operator, acceleration response data was collected on a complex aluminum structure excited by stationary random vibration while simultaneously impacted by three different modal hammers. The force output of each hammer was recorded to corroborate the impacts in time with IIF results. Detection efficacy of the IIF is benchmarked using the scale-averaged wavelet power and the local Hölder exponent representing changes in signal energy and continuity, respectively. Results show that IIF analysis is highly effective at detecting impacts. General behavior of the IIF is discussed with specific examples.