Fault diagnosis of rolling bearings based on multifractal detrended fluctuation analysis and Mahalanobis distance criterion

Lin, J. H.; Qian, Chen

doi:10.1016/j.ymssp.2012.12.014

Cited by 121 publications

(54 citation statements)

References 46 publications

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“…Afterwards, an extension of DFA called multifractal detrending fluctuation analysis (MFDFA) was proposed by Kantelhardt et al for examining the multifractality of non-stationary time series in 2002 [29]. Currently, MFDFA has been successfully applied to analyze various data, such as hydrographic data [30], wind records [31], financial time series [32], traffic time series [33], control system assessment [34], mechanical vibration signals [35], etc. It has proven to be a powerful tool for uncovering the multifractality of non-stationary time series in the complex systems.…”

Section: Mfdfa Algorithmmentioning

confidence: 99%

Extraction of Coal and Gangue Geometric Features with Multifractal Detrending Fluctuation Analysis

2018

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Abstract:The separation of coal and gangue is an important process of the coal preparation technology. The conventional way of manual selection and separation of gangue from the raw coal can be replaced by computer vision technology. In the literature, research on image recognition and classification of coal and gangue is mainly based on the grayscale and texture features of the coal and gangue. However, there are few studies on characteristics of coal and gangue from the perspective of their outline differences. Therefore, the multifractal detrended fluctuation analysis (MFDFA) method is introduced in this paper to extract the geometric features of coal and gangue. Firstly, the outline curves of coal and gangue in polar coordinates are detected and achieved along the centroid, thereby the multifractal characteristics of the series are analyzed and compared. Subsequently, the modified local singular spectrum widths ∆h of the outline curve series are extracted as the characteristic variables of the coal and gangue for pattern recognition. Finally, the extracted geometric features by MFDFA combined with the grayscale and texture features of the images are compared with other methods, indicating that the recognition rate of coal gangue images can be increased by introducing the geometric features.

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Section: Mfdfa Algorithmmentioning

confidence: 99%

Extraction of Coal and Gangue Geometric Features with Multifractal Detrending Fluctuation Analysis

2018

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“…MFDFA can estimate the multifractal spectrum of the generalized Hurst exponent from a time series and it does not require any knowledge about the process time delay or other process parameters [27]. Currently, MFDFA has been successfully applied to analyze various data, such as hydrographic data [28], wind records [29], financial time series [30], traffic time series [31], mechanical vibration signals [32], etc. It has proven to be a powerful tool for uncovering the multifractality of non-stationary time series in the complex systems.…”

Section: Mfdfa Algorithmmentioning

confidence: 99%

A Novel Method for Control Performance Assessment with Fractional Order Signal Processing and Its Application to Semiconductor Manufacturing

et al. 2018

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Abstract:The significant task for control performance assessment (CPA) is to review and evaluate the performance of the control system. The control system in the semiconductor industry exhibits a complex dynamic behavior, which is hard to analyze. This paper investigates the interesting crossover properties of Hurst exponent estimations and proposes a novel method for feature extraction of the nonlinear multi-input multi-output (MIMO) systems. At first, coupled data from real industry are analyzed by multifractal detrended fluctuation analysis (MFDFA) and the resultant multifractal spectrum is obtained. Secondly, the crossover points with spline fit in the scale-law curve are located and then employed to segment the entire scale-law curve into several different scaling regions, in which a single Hurst exponent can be estimated. Thirdly, to further ascertain the origin of the multifractality of control signals, the generalized Hurst exponents of the original series are compared with shuffled data. At last, non-Gaussian statistical properties, multifractal properties and Hurst exponents of the process control variables are derived and compared with different sets of tuning parameters. The results have shown that CPA of the MIMO system can be better employed with the help of fractional order signal processing (FOSP).

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“…MF-DFA, a conventional statistical method, is widely used in image processing, data mining, biological engineering, environment, fault diagnosis, and so forth. The generalized MF-DFA procedure mainly consists of five steps [24]. In the first three steps, the procedural contents are essentially identical to the conventional DFA method.…”

Section: Multifractal Detrended Fluctuation Analysis Multifractal Dementioning

confidence: 99%

Fault Diagnosis of Broken Rotor Bars in Squirrel-Cage Induction Motor of Hoister Based on Duffing Oscillator and Multifractal Dimension

Zhao

Wang

Zhang

2014

Advances in Mechanical Engineering

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This paper is to propose a novel fault diagnosis method for broken rotor bars in squirrel-cage induction motor of hoister, which is based on duffing oscillator and multifractal dimension. Firstly, based on the analysis of the structure and performance of modified duffing oscillator, the end of transitional slope from chaotic area to large-scale cycle area is selected as the optimal critical threshold of duffing oscillator by bifurcation diagrams and Lyapunov exponent. Secondly, the phase transformation duffing oscillator from chaos to intermittent chaos is sensitive to the signals, whose frequency difference is quite weak from the reference signal. The spectrums of the largest Lyapunov exponents and bifurcation diagrams of the duffing oscillator are utilized to analyze the variance in different parameters of frequency. Finally, this paper is to analyze the characteristics of both single fractal (box-counting dimension) and multifractal and make a comparison between them. Multifractal detrended fluctuation analysis is applied to detect extra frequency component of current signal. Experimental results reveal that the method is effective for early detection of broken rotor bars in squirrel-cage induction motor of hoister.

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Fault diagnosis of rolling bearings based on multifractal detrended fluctuation analysis and Mahalanobis distance criterion

Cited by 121 publications

References 46 publications

Extraction of Coal and Gangue Geometric Features with Multifractal Detrending Fluctuation Analysis

Extraction of Coal and Gangue Geometric Features with Multifractal Detrending Fluctuation Analysis

A Novel Method for Control Performance Assessment with Fractional Order Signal Processing and Its Application to Semiconductor Manufacturing

Fault Diagnosis of Broken Rotor Bars in Squirrel-Cage Induction Motor of Hoister Based on Duffing Oscillator and Multifractal Dimension

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