Fault Diagnosis Using Adaptive Multifractal Detrended Fluctuation Analysis

Du, Wenliao; Kang, Myeongsu; Pecht, Michael

doi:10.1109/tie.2019.2892667

Cited by 34 publications

(26 citation statements)

References 23 publications

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“…The wavelet transform modulus maxima (WTMM) and multifractal detrended fluctuation analysis (MF-DFA) are two typical multifractal methods often used for fault diagnosis. Du et al (2019) indicated that multifractal analysis is efficient to analyze non-uniformity and singularity of nonstationary and nonlinear signals. It is noted that WTMM uses the continuous wavelet transform with high computational complexity and is not suitable for real-time diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Chatter detection for milling using novel p-leader multifractal features

Chen

Hou

et al. 2020

J Intell Manuf

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Chatter in machining results in poor workpiece surface quality and short tool life. A reliable chatter detection method is needed to monitor this self-excited vibration before its complete development. This paper applies multifractal features extracted from a novel p-leader multifractal formalism for chatter detection in milling processes. This novel formalism is able to discover a complex singular behavior rising on unstable chatter signals, and improves chatter detection performance. The multifractal features are selected using a multivariate filter, and assessed in terms of their dynamic monitoring abilities and classification accuracies under wide cutting conditions. The results show that the multifractal features cannot only detect chatter with high accuracy and short computation time, but are also efficient to distinguish stable tests with large amplitudes from unstable tests. A small p is recommended to highlight dynamic variations and obtain high classification accuracies. For further verification, the

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Section: Introductionmentioning

confidence: 99%

Chatter detection for milling using novel p-leader multifractal features

Chen

Hou

et al. 2020

J Intell Manuf

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“…However, either of WT and EMD seemingly encounters some difficulties in analyzing complex machinery vibration signals [9][10][11][12]. A lot of references have indicated that vibration signals from defective rotating machinery display obvious fractal and chaotic properties [1,[13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…the embedding dimension and the time delay [20]. Next, mutifractal detrended fluctuation analysis (MFDFA) was applied to examine dynamics of complex machinery vibration signals [1,14,16,17]. Nonetheless, MFD-FA needs refining further since suffering from some shortages [1,17].…”

Section: Introductionmentioning

confidence: 99%

Feature extraction based on generalized permutation entropy for condition monitoring of rotating machinery

2021

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Defective rotating machinery usually exhibits complex dynamic behavior. Therefore, feature representation of machinery vibration signals is always critical for condition monitoring of rotating machinery. Permutation entropy (PeEn), an adaptive symbolic description, can measure complexities of signals. However, PeEn may lack the capability to fully describe dynamics of complex signals since compressing all the information into a single parameter. Afterwards, multiscale PeEn (MPeEn) is put forward for coping with nonstationarity, outliers and artifacts emerging in complex signals. In MPeEn, a set of parameters serve to describe dynamics of a complex signal in different time scales. Nonetheless, an average procedure in MPeEn may withhold local information of a complex signal. To overcome deficiencies of PeEn and MPeEn, this paper proposes generalized PeEn (GPeEn) by introducing different time lags and orders into PeEn. In GPeEn, a complex signal is compressed into one matrix rather than a single parameter. Moreover, minimal, maximal and average values of a matrix obtained by GPeEn serve to briefly describe conditions of rotating machinery. Next, a numerical experiment proves that GPeEn outperforms PeEn and MPeEn in characterizing conditions of a Lorenz model. Subsequently, the performance of GPeEn is benchmarked against that of PeEn and MPeEn by investigating gear and roll-bearing vibration signals containing different types and severity of faults. The results show that the proposed method has a clear advantage over PeEn and MPeEn in condition monitoring of rotating machinery.

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“…The widely used monofractal or detrended fluctuation analysis (DFA) does not give any information about local fractal components but it describes the overall fractal features in a non‐stationary or non‐linear signal, thereby decreasing the robustness of this approach 21 . Recently, a novel and robust tool named as multifractal detrended fluctuation analysis (MFDFA) 21,27‐30 has been proposed which is an extension of DFA to analyze the multifractality or local fractal components of a non‐stationary signals such as wind records, combustion flame fluctuations, sunspot time series, biomedical time series, geographical and hydrographic data, traffic time series, temperature series, partial discharge and bearing motor fault data, 27,29,30 etc.…”

Section: Introductionmentioning

confidence: 99%