Search

Vibrations of a defective rolling bearing often exhibit nonstationary and nonlinear characteristics which are submerged in strong noise and interference components. Thus, diagnostic feature extraction is always a challenge and has aroused wide concerns for a long time. In this paper, the multifractal detrended fluctuation analysis (MF-DFA) is applied to uncover the multifractality buried in nonstationary time series for exploring rolling bearing fault data. Subsequently, a new approach for fault diagnosis is proposed based on MF-DFA and Mahalanobis distance criterion. The multifractality of bearing data is estimated with the generalized the Hurst exponent and the multifractal spectrum. Five characteristic parameters which are sensitive to changes of bearing fault conditions are extracted from the spectrum for diagnosis of fault sizes. For benchmarking this new method, the empirical mode decomposition (EMD) method is also employed to analyze the same dataset. The results show that MF-DFA outperforms EMD in revealing the nature of rolling bearing fault data. [ABSTRACT FROM PUBLISHER]

We analyse symbolic sequences of a large number of known long noncoding genes in a variety of organisms via their Fourier and wavelet transforms. Although these do not have the striking correlation structure of protein coding genes, there are significant patterns within the sequences which can be detected through spectral methods. While these methods are not by themselves of sufficient accuracy to detect noncoding genes, they can provide useful filters to be used in conjunction with other tools for the identification of noncoding genes. [ABSTRACT FROM AUTHOR]

We analyse symbolic sequences of a large number of known long noncoding genes in a variety of organisms via their Fourier and wavelet transforms. Although these do not have the striking correlation structure of protein coding genes, there are significant patterns within the sequences which can be detected through spectral methods. While these methods are not by themselves of sufficient accuracy to detect noncoding genes, they can provide useful filters to be used in conjunction with other tools for the identification of noncoding genes. [ABSTRACT FROM AUTHOR]

Many physical and biological systems exhibit complex behavior characterized by long-range power-law correlations. Detrended fluctuation analysis (DFA) is a scaling analysis method that provides a scaling parameter to represent the correlation properties of a signal. The study of interbeat sequences with the DFA method has revealed the presence of crossovers associated with physiological aging and heart with failure; the hinges present in the crossover region from both the elderly healthy individuals and the patients with congestive heart failure (CHF) are in opposite directions. The interbeat sequences of healthy young persons do not show crossovers. In this paper we study interbeat time series of healthy young and elderly persons and patients with CHF. We use the DFA-m method, where m refers to the order of the polynomial function used for the fitting. For instance, DFA-2 filters linear trends and DFA-3 filters quadratic trends. We found that the presence of the crossovers and the direction of the hinges are conserved when we apply the DFA method for different values of m. Therefore we conclude that the DFA-m method is a reliable method to accurately quantify correlations in interbeat time series even if there are polynomial trends. We can characterize the crossovers and we can conclude that the crossovers are not a result of the trends; they are part of the system dynamics. © 2006 American Institute of Physics [ABSTRACT FROM AUTHOR]

Detrended fluctuation analysis is adopted to research the fractal scaling property of sea clutter. Analysis results show that scale crossover exists and it is due to the discrepancy in correlation properties of sea clutter at different scales. The fit error to linear curves between sea clutter and target bins differs, which enables the effective detection of targets within sea clutter. Detection performance with real data validates the effectiveness of the algorithm. [ABSTRACT FROM PUBLISHER]