Refine
Document Type
- Article (3)
Language
- English (3)
Has Fulltext
- yes (3)
Is part of the Bibliography
- yes (3)
Keywords
- Materialermüdung (2)
- Leistungsdichte <Physik> (1)
- Sonogramm (1)
Designing mechanical structures exposed to random vibration loading compromises the central challenges of defining comprehensive load assumptions and of processing these efficiently in a fatigue assessment. For this matter of statistical load description, frequency-domain methods withhold major advantages. They describe random vibration loading by its power spectral density, which allows drastic data reduction, to conduct efficient response analyses and to derive a statistical description of resulting load spectra. Nevertheless, this procedure is limited to stationary Gaussian loading. Thus, this paper proposes an extension of the frequency-domain approach to a special class of non-stationary loading – amplitude-modulated processes. These consist of a unique vibration state that varies in intensity, which is represented by a modulating signal. This paper develops a methodology to test for amplitude-modulated processes, to derive efficient measures for the intensity variation and to include this behavior in a fatigue assessment carried out in frequency-domain. The full methodology is presented via a set of simulated data.
The power spectral density (PSD) is a fundamental technique of random vibration fatigue providing an effective statistical characterization that can be processed by linear systems theory and load spectrum estimators. This lays the basis for a statistical-based fatigue assessment. While the PSD assembles a full stochastic characterization of stationary Gaussian loading, for loading subjected to changing operational, environmental, and excitational conditions, it provides no means of a fluctuating spectral density. Therefore, the PSD neither qualifies to characterize varying loads, nor reproduces comparable stress amplitudes to a referencing non-stationary excitation following a statistical-based stress analysis. Consequently, this paper employs the non-stationarity matrix to characterize the varying evolution of realistic loading and proposes a system of equations that decomposes this characterization into stationary Gaussian portions. The fundamental idea is to approximate realistic loading by abstracting a series of stationary segments, whose assembly in return embodies a full statistical characterization. The resulting quasi-stationary load definition better reflects the fatigue damage potential of realistic, non-stationary loading and allows to implement load spectrum estimators, ensuing computationally efficient and statistically robust structural lifetime predictions. Further, quasi-stationary load definitions are utilized to advance the concept of damage-equivalent statistical load definitions to be independent of a specific Miner exponent.
This paper addresses the use of higher-order spectra to study the non-Gaussian nature of random vibration loading. Since the power spectral density is only a full description for stationary Gaussian processes, specifying non-Gaussian random vibration loading requires a sophisticated statistical description. In recent research higher-order statistical moments such as skewness and kurtosis have been used to de_ne non-Gaussian properties of vibration loading. However, useful information contained in the spectral representation of these moments is neglected. This paper introduces the trispectrum as a tool for analyzing vibration loading. It is the spectral representation of the fourth-order moment and thus extends the information content of the kurtosis. For demonstration several common methods for generating non-Gaussian loading are reviewed and used to derive loads that reproduce the power spectral density and kurtosis of a real in-service loading. These loads are analyzed using Fatigue Damage Spectra and trispectra to relate structural response behavior to their non-Gaussian nature. The results suggest that the trispectrum is a valuable tool for analyzing and classifying non-Gaussian random loading.