The primary goal of any laboratory test is to expose the unit-under-test to conservative realistic representations of a field environment. Satisfying this objective is not always straightforward due to laboratory equipment constraints. For vibration and shock tests performed on shakers over-testing and unrealistic failures can result because the control is a base acceleration and mechanical shakers have nearly infinite impedance. Force limiting and response limiting are relatively standard practices to reduce over-test risks in random-vibration testing. Shaker controller software generally has response limiting as a built-in capability and it is done without much user intervention since vibration control is a closed loop process. Limiting in shaker shocks is done for the same reasons, but because the duration of a shock is only a few milliseconds, limiting is a pre-planned user in the loop process. Shaker shock response limiting has been used for at least 30 years at Sandia National Laboratories, but it seems to be little known or used in industry. This objective of this paper is to re-introduce response limiting for shaker shocks to the aerospace community. The process is demonstrated on the BARBECUE testbed.
There is a wide variety of applications that subject systems to mechanical shock and vibration environments. How to best characterize those environments and generate the necessary system and component test specifications varies according to the nature of the underlying environment. The purpose of this paper is to provide the reader with an overview of some commonly used analysis techniques for a range of field environments including transportation and handling, aircraft carriage, and missile flight. The paper will also address statistical methods for defining the Maximum Predicted Environment and test control methods as they pertain to achieving the best possible system and component laboratory simulations.
There is a wide variety of applications that subject systems to mechanical shock and vibration environments. How to best characterize those environments and generate the necessary system and component test specifications varies according to the nature of the underlying environment. The purpose of this paper is to provide the reader with an overview of some commonly used analysis techniques for a range of field environments including transportation and handling, aircraft carriage, and missile flight. The paper will also address statistical methods for defining the Maximum Predicted Environment and test control methods as they pertain to achieving the best possible system and component laboratory simulations.
In random vibration environments, sinusoidal line noise may appear in the vibration signal and can affect analysis of the resulting data. We studied two methods which remove stationary sine tones from random noise: a matrix inversion algorithm and a chirp-z transform algorithm. In addition, we developed new methods to determine the frequency of the tonal noise. The results show that both of the removal methods can eliminate sine tones in prefabricated random vibration data when the sine-to-random ratio is at least 0.25. For smaller ratios down to 0.02 only the matrix inversion technique can remove the tones, but the metrics to evaluate its effectiveness also degrade. We also found that using fast Fourier transforms best identified the tonal noise, and determined that band-pass-filtering the signals prior to the process improved sine removal. When applied to actual vibration test data, the methods were not as effective at removing harmonic tones, which we believe to be a result of mixed-phase sinusoidal noise.
The purpose of this research is to describe and illustrate practical methods that can be used to construct tolerance bounds for a multivariate measurement associated with a covariate. These methods rely on principal components analysis and the parametric bootstap. The methods are illustrated with an example in which the vibration environment experienced by a test object being carried by an aircraft (known as captive carry) is characterized.
One of the more severe environments for a store on an aircraft is during the ejection of the store. During this environment it is not possible to instrument all component responses, and it is also likely that some instruments may fail during the environment testing. This work provides a method for developing these responses from failed gages and uninstrumented locations. First, the forces observed by the store during the environment are reconstructed. A simple sampling method is used to reconstruct these forces given various parameters. Then, these forces are applied to a model to generate the component responses. Validation is performed on this methodology.