Publications

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The summary of this report is: (1) The Kernal Density Estimator (KDE) model using log data provides the most conservative estimates; (2) The Empirical Tolerance Limit (ETL) model provides the least conservative estimates; (3) The results for the Karhunen-Loeve (K-L) and Normal Tolerance Limit (NTL) models lie in between the extremes; (4) The NTL results ended up being as credible as any of the other methods. This may be related to the fact that the data appeared to fit a lognormal distribution for higher values of {beta}; (5) The discrepancy between these methods appears to widen for higher values of {beta} and {gamma}; (6) The reasons for the extreme difference in the KDE results depending on whether one uses the raw data or the log of the data is not clear at this time; and (7) Which model will best suit our needs is not clear at this time.

Transportation of sensitive flight hardware requires information about the expected transportation environment as well as the actual transportation environment during the part's movement--typically vibration with superimposed intermittent shocks. Each data type has different sampling, processing, and specification requirements. Analyzing shock data requires high sampling rates and leads to large file sizes. A barrier to analyzing data has been the vast quantity of information acquired. Previous approaches have focused either on manually separating data or on selectively recording extreme data. The use of an automated approach allows for quickly verifying vibration and shock levels while retaining the robustness of the underlying data set. Further, the automated approach allows the environments engineer to select criteria for shock/vibration sorting, which removes the subjectivity associated with visual differentiation. This automated technique evaluated several vehicles over four different road conditions in the same time that one data set could have been processed using visual discrimination. Automated processing of satellite shipment vibration and shock data is made thoroughly and objectively vs. traditional shock and tilt indicators. The automated technique could also be useful in processing large amounts of on-orbit data for changes in vibration signature.

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One challenge faced by engineers today is replicating an operating environment such as transportation in a test lab. This paper focuses on the process of identifying sine-on-random content in an aircraft transportation environment, although the methodology can be applied to other events. The ultimate goal of this effort was to develop an automated way to identify significant peaks in the PSDs of the operating data, catalog the peaks, and determine whether each peak was sinusoidal or random in nature. This information helps design a test environment that accurately reflects the operating environment. A series of Matlab functions have been developed to achieve this goal with a relatively high degree of accuracy. The software is able to distinguish between sine-on-random and random-on-random peaks in most cases. This paper describes the approach taken for converting the time history segments to the frequency domain, identifying peaks from the resulting PSD, and filtering the time histories to determine the peak amplitude and characteristics. This approach is validated through some contrived data, and then applied to actual test data. Observations and conclusions, including limitations of this process, are also presented.

One challenge faced by engineers today is replicating an operating environment such as transportation in a test lab. This paper focuses on the process of identifying sine-on-random content in an aircraft transportation environment, although the methodology can be applied to other events. The ultimate goal of this effort was to develop an automated way to identify significant peaks in the PSDs of the operating data, catalog the peaks, and determine whether each peak was sinusoidal or random in nature. This information helps design a test environment that accurately reflects the operating environment. A series of Matlab functions have been developed to achieve this goal with a relatively high degree of accuracy. The software is able to distinguish between sine-on-random and random-on-random peaks in most cases. This paper describes the approach taken for converting the time history segments to the frequency domain, identifying peaks from the resulting PSD, and filtering the time histories to determine the peak amplitude and characteristics. This approach is validated through some contrived data, and then applied to actual test data. Observations and conclusions, including limitations of this process, are also presented.

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