Abstract
The similarity between a shock response spectrum (SRS) and a target shock specification is essential in evaluating the success of a qualification test of a space component. Qualification testing facilities often utilize shock response databases for rapid testing. Traditionally, the comparison of two shocks (SRS) depends on visual evaluation, which is, at best, subjective. This paper compares five different quantitative methods for evaluating shock response similarity. This work aims to find the most suitable metric for retrieving an SRS from a pyroshock database. The five methods are the SRS difference, mean acceleration difference, average SRS ratio, dimensionless SRS coefficients, and mean square goodness-of-fit method. None of the similarity metrics account for the sign of the deviation between the target SRS and database SRS, making it challenging to satisfy the criteria for a good shock test. We propose a metric (the weighted distance) for retrieving the most similar SRS to a target SRS specification from a shock database in this work. The weighted distance outperforms the mean square goodness-of-fit and other metrics in database SRS retrieval for rapid qualification testing.
Highlights
Small electronic components such as chips and crystals used in satellite-electronic boards are susceptible to the pyrotechnic shock events during a rocket launch
We evaluated five shock response spectrum (SRS) similarity metrics for suitability in retrieving an SRS with a similar profile to a target SRS specification from a shock test database
All evaluated metrics are calculated from the absolute deviation between the SRS magnitudes. e SRS’s absolute deviation does not account for the sign of the deviations of the database SRS from the target SRS
Summary
Small electronic components such as chips and crystals used in satellite-electronic boards are susceptible to the pyrotechnic shock events during a rocket launch. There are many tools, such as the energy spectrum (Fourier transform), shock response spectrum (SRS), temporal moments, and wavelets for comparing signal features [2] Most of these tools’ starting point is the acceleration-time history, which is not usually available as a shock test specification. E SRS was first described in a thesis on the transient oscillations in elastic systems by Maurice Biot [4] Since it has found applications in the characterization of aerospace components’ mechanical shock, upon sudden impact, or in the explosive (pyrotechnic) separation of spacecraft rocket stages [5]. A shock data exhibiting zero shift is still useful for evaluating the SRS in the high-frequency region (>1 kHz). Our policy is to repeat a test whenever the data exhibits a zero shift
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