A Research on Local Mean in Empirical Mode Decomposition

As a multichannel signal processing method based on data-driven, multivariate empirical mode decomposition (MEMD) has attracted much attention due to its potential ability in self-adaption and multi-scale decomposition for multivariate data. Commonly, the uniform projection scheme on a hypersphere is used to estimate the local mean. However, the unbalanced data distribution in high-dimensional space often conflicts with the uniform samples and its performance is sensitive to the noise components. Considering the common fact that the vibration signal is generated by three sensors located in different measuring positions in the domain of the structural health monitoring for the key equipment, thus a novel trivariate empirical mode decomposition via convex optimization was proposed for rolling bearing condition identification in this paper. For the trivariate data matrix, the low-rank matrix approximation via convex optimization was firstly conducted to achieve the denoising. It is worthy to note that the non-convex penalty function as a regularization term is introduced to enhance the performance. Moreover, the non-uniform sample scheme was determined by applying singular value decomposition (SVD) to the obtained low-rank trivariate data and then the approach used in conventional MEMD algorithm was employed to estimate the local mean. Numerical examples of synthetic defined by the fault model and real data generated by the fault rolling bearing on the experimental bench are provided to demonstrate the fruitful applications of the proposed method.

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Improving empirical mode decomposition for vibration signal analysis

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406216630004 ◽

2016 ◽

Vol 231 (12) ◽

pp. 2223-2234 ◽

Cited By ~ 2

Author(s):

Mousa Rezaee ◽

Amin Taraghi Osguei

Keyword(s):

Empirical Mode Decomposition ◽

Decomposition Method ◽

Vibration Signal ◽

Mode Decomposition ◽

Empirical Mode Decomposition Method ◽

Local Mean ◽

The Common ◽

Vibration Signal Analysis ◽

Overshoot And Undershoot ◽

Better Than

In this paper, the empirical mode decomposition as a signal processing method has been studied to overcome one of its shortcomings. In the previous studies, some improvements have been made on the empirical mode decomposition and it has been applied for condition monitoring of mechanical systems. These improvements include elimination of mode mixing and restraining of end effect in empirical mode decomposition method. In this research, to increase the accuracy of empirical mode decomposition, a new local mean has been proposed in the sifting process. Through the proposed local mean, the overshoot and undershoot problems in defining the local mean of common algorithm are alleviated. Meanwhile, it is capable to separate the components with close frequencies. Through the analysis of simulated signals via the new algorithm, it is shown that the accuracy is improved. Finally, empirical mode decomposition-based fault diagnosis approach has been applied to a vibration signal obtained from a faulty gearbox. The results show that the proposed method can resolve the effects of damage in vibration signals better than the common empirical mode decomposition method and helps for the isolation and localization of the fault.

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Denoising of Electrocardiogram (ECG) signal by using empirical mode decomposition (EMD) with non-local mean (NLM) technique

Journal of Applied Biomedicine ◽

10.1016/j.bbe.2018.01.005 ◽

2018 ◽

Vol 38 (2) ◽

pp. 297-312 ◽

Cited By ~ 22

Author(s):

Shailesh Kumar ◽

Damodar Panigrahy ◽

P.K. Sahu

Keyword(s):

Empirical Mode Decomposition ◽

Ecg Signal ◽

Mode Decomposition ◽

Local Mean ◽

Non Local ◽

Electrocardiogram Ecg

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The joint empirical mode decomposition-local mean decomposition method and its application to time series of compressor stall process

Aerospace Science and Technology ◽

10.1016/j.ast.2020.105969 ◽

2020 ◽

Vol 105 ◽

pp. 105969

Author(s):

Shaoyuan Yue ◽

Yangang Wang ◽

Liguo Wei ◽

Zheng Zhang ◽

Hao Wang

Keyword(s):

Time Series ◽

Empirical Mode Decomposition ◽

Decomposition Method ◽

Local Mean Decomposition ◽

Mode Decomposition ◽

Local Mean

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A Comparative Study on the Local Mean Decomposition and Empirical Mode Decomposition and Their Applications to Rotating Machinery Health Diagnosis

Journal of Vibration and Acoustics ◽

10.1115/1.4000770 ◽

2010 ◽

Vol 132 (2) ◽

Cited By ~ 106

Author(s):

Yanxue Wang ◽

Zhengjia He ◽

Yanyang Zi

Keyword(s):

Empirical Mode Decomposition ◽

Instantaneous Frequency ◽

Early Stage ◽

Adaptive Methods ◽

Rotating Machinery ◽

Local Mean Decomposition ◽

Mode Decomposition ◽

Excited Vibration ◽

Local Mean ◽

Health Diagnosis

Health diagnosis of the rotating machinery can identify potential failure at its early stage and reduce severe machine damage and costly machine downtime. In recent years, the adaptive decomposition methods have attracted many researchers’ attention, due to less influences of human operators in the practical application. This paper compares two adaptive methods: local mean decomposition (LMD) and empirical mode decomposition (EMD) from four aspects, i.e., local mean, decomposed components, instantaneous frequency, and the waveletlike filtering characteristic through numerical simulation. The comparative results manifest that more accurate instantaneous frequency and more meaningful interpretation of the signals can be acquired by LMD than by EMD. Then LMD and EMD are both exploited in the health diagnosis of two actual industrial rotating machines with rub-impact and steam-excited vibration faults, respectively. The results reveal that LMD seems to be more suitable and have better performance than EMD for the incipient fault detection. LMD is thus proved to have potential to become a powerful tool for the surveillance and diagnosis of rotating machinery.

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Feature Extraction of EEG Signals Based on Local Mean Decomposition and Fuzzy Entropy

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420580173 ◽

2020 ◽

Vol 34 (12) ◽

pp. 2058017

Author(s):

Yanping Li ◽

Qi Wang ◽

Tao Wang ◽

Jian Pei ◽

Shuo Zhang

Keyword(s):

Feature Extraction ◽

Empirical Mode Decomposition ◽

Extraction Method ◽

Decomposition Method ◽

Fuzzy Entropy ◽

Eeg Signals ◽

Feature Extraction Method ◽

Mode Decomposition ◽

Empirical Mode Decomposition Method ◽

Local Mean

An improved feature extraction method is proposed aiming at the recognition of motor imagined electroencephalogram (EEG) signals. Using local mean decomposition, the algorithm decomposes the original signal into a series of product function (PF) components, and meaningless PF components are removed from EEG signals in the range of mu rhythm and beta rhythm. According to the principle of feature time selection, 4[Formula: see text]s to 6[Formula: see text]s motor imagery EEG signals are selected as classification data, and the sum of fuzzy entropies of second-and third-order PF components of [Formula: see text], [Formula: see text] lead signals is calculated, respectively. Mean value of fuzzy entropy [Formula: see text] is used as input element to construct EEG feature vector, and support vector machine (SVM) is used to classify and predict EEG signals for recognition. The test results show that this feature extraction method has higher classification accuracy than the empirical mode decomposition method and the total empirical mode decomposition method.

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Multivariate empirical mode decomposition

Proceedings of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rspa.2009.0502 ◽

2009 ◽

Vol 466 (2117) ◽

pp. 1291-1302 ◽

Cited By ~ 441

Author(s):

N. Rehman ◽

D. P. Mandic

Keyword(s):

Empirical Mode Decomposition ◽

Human Motion ◽

Time Frequency ◽

Quasi Monte Carlo ◽

Multivariate Empirical Mode Decomposition ◽

Low Discrepancy Sequences ◽

Mode Decomposition ◽

Stationary Signals ◽

Local Mean ◽

Multivariate Signals

Despite empirical mode decomposition (EMD) becoming a de facto standard for time-frequency analysis of nonlinear and non-stationary signals, its multivariate extensions are only emerging; yet, they are a prerequisite for direct multichannel data analysis. An important step in this direction is the computation of the local mean, as the concept of local extrema is not well defined for multivariate signals. To this end, we propose to use real-valued projections along multiple directions on hyperspheres ( n -spheres) in order to calculate the envelopes and the local mean of multivariate signals, leading to multivariate extension of EMD. To generate a suitable set of direction vectors, unit hyperspheres ( n -spheres) are sampled based on both uniform angular sampling methods and quasi-Monte Carlo-based low-discrepancy sequences. The potential of the proposed algorithm to find common oscillatory modes within multivariate data is demonstrated by simulations performed on both hexavariate synthetic and real-world human motion signals.

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