Multifractal characteristic analysis of near-fault earthquake ground motions

Yang, Dan; Zhang, Changgeng; Liu, Yunhe

doi:10.1016/j.soildyn.2015.01.020

Cited by 13 publications

(3 citation statements)

References 42 publications

(76 reference statements)

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“…Thus, a multifractal analysis method of EMD-LS-MFDFA is proposed to address this problem. The proposed approach integrates the automatic determination of EMD trend term method [17,18] and the least squares (LS) method [19]. This removes limitations associated with the complete decomposition of EMD, while simultaneously overcoming the defects of modal confusion and the endpoint effect.…”

Section: Improved Mfdfa Methodsmentioning

confidence: 99%

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Application of Improved MFDFA and D-S Evidence Theory in Fault Diagnosis

et al. 2022

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To improve the accuracy of centrifugal pump fault diagnosis, a novel fault diagnosis method based on improved multiple fractal detrended fluctuation analysis (MFDFA), the fusion of multi-sensing information derived from the back propagation (BP) neural network and the Dempster–Shafter (D-S) evidence theory, is accordingly proposed. Firstly, the multifractal spectral parameters of four sensor signals under four different operating conditions were extracted as centrifugal pump fault feature vectors using improved MFDFA and input to the BP neural network. Then, the basic trust assignment function was constructed by calculating trustworthiness (both local and global) as priori information, which is based on the output results of the neural networks specific to of each group of sensors. Finally, the basic trust assignment function was fused with decision processing in accordance with the D-S evidence combination rule in order to effectively achieve the multi-sensor information fusion centrifugal pump fault diagnosis. The experimental results show the multiple fractal spectrum feature parameters extracted by the improved MFDFA can accurately reflect the signal essence, and can be used as the fault feature vector. On this basis, this multi-sensor fault diagnosis reduces the uncertainty of fault classification and demonstrates improved accuracy compared to the single-sensor fault diagnosis thanks to being based on a combination of the BP neural networks and D-S evidence theory. Thereby, this method can facilitate accurate diagnosis of the centrifugal pump fault type with high confidence, subsequently providing a novel and alternative method to existing methods of diagnosis.

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Section: Improved Mfdfa Methodsmentioning

confidence: 99%

“…In turn, for the signal x (i), the MFDFA method is applied to carry out the feature extraction [18], i.e., the signal can be expressed as a q fluctuation function of order.…”

Section: Improved Mfdfa Methodsmentioning

confidence: 99%

Application of Improved MFDFA and D-S Evidence Theory in Fault Diagnosis

et al. 2022

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“…Therefore, DFA has been demonstrated to be a robust method for determining the scaling behavior of noisy data. It has been successfully applied to identify various features of time series such as heat rate variability [30,32], neuronal oscillations [33], human gait signals [34], economic trend [35], weather records [36], gearbox and bearing fault diagnosis [37,38], and seismic analysis [39].…”

Section: Introductionmentioning

confidence: 99%

Damage detection of structures with detrended fluctuation and detrended cross-correlation analyses

Lin¹,

Fajri²

2017

Smart Mater. Struct.

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Recently, fractal analysis has shown its potential for damage detection and assessment in fields such as biomedical and mechanical engineering. Because of its practicability in interpreting irregular, complex, and disordered phenomena, a structural health monitoring (SHM) system based on detrended fluctuation analysis (DFA) and detrended crosscorrelation analysis (DCCA) is proposed. First, damage conditions can be swiftly detected by evaluating ambient vibration signals measured from a structure through DFA. Damage locations can then be determined by analyzing the cross correlation of signals of different floors by applying DCCA. A damage index is also proposed based on multi-scale DCCA curves to improve the damage location accuracy. To verify the performance of the proposed SHM system, a four-story numerical model was used to simulate various damage conditions with different noise levels. Furthermore, an experimental verification was conducted on a seven-story benchmark structure to assess the potential damage. The results revealed that the DFA method could detect the damage conditions satisfactorily, and damage locations can be identified through the DCCA method with an accuracy of 75%. Moreover, damage locations can be correctly assessed by the damage index method with an improved accuracy of 87.5%. The proposed SHM system has promising application in practical implementations.

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