Elimination of end effects in local mean decomposition using spectral coherence and applications for rotating machinery

Guo, Wei; Huang, Lingjian; Chen, Cheng; Zou, Huaiwu; Liu, Zhiwen

doi:10.1016/j.dsp.2016.04.007

Cited by 49 publications

(29 citation statements)

References 24 publications

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“…The reliability of such extension will sharply decrease as its distance away from the known data set increases, and thus it is necessary to be careful in extending a signal only by adding the extrapolation data to it [10]. Otherwise, the error of such operation would propagate from the end to the interior of the data and even cause severe deterioration of the whole signal [9].…”

Section: Improvements For Eliminating End Effectsmentioning

confidence: 99%

“…Meanwhile, the extension based on characteristics of the signal waveform seems to be more appropriate to describe such complexity of problems [10]. In the following section, an adaptive waveform extension method [9] is introduced to extend vibration signals and avoid error accumulation.…”

Section: Improvements For Eliminating End Effectsmentioning

confidence: 99%

“…Motivated by this idea, some extension methods tried to extend the temporal waveforms forward and backward by using all available information in data, including feature-based extension, mirror or anti-mirror extension, intelligent prediction, pattern comparison, etc. [9].…”

Section: Improvements For Eliminating End Effectsmentioning

confidence: 99%

“…An adaptive extension method [9] was designed for vibration signals, mainly including three steps: waveform segmentation, spectral coherence comparison and waveform extension. Its main idea is to automatically search inside waveforms having mostly similar frequency spectrum to ends, and then use their successive segments for signal extension.…”

Section: Adaptive Data Extension-based Spectral Coherencementioning

confidence: 99%

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Adaptive Signal Decomposition Methods for Vibration Signals of Rotating Machinery

Guo¹,

Zuo²

2017

Fault Diagnosis and Detection

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Vibration-based condition monitoring and fault diagnosis are becoming more common in the industry to increase machine availability and reliability. Considerable research efforts have recently been directed towards the development of adaptive signal processing methods for fault diagnosis. Two adaptive signal decomposition methods, i.e. the empirical mode decomposition (EMD) and the local mean decomposition (LMD), are widely used. This chapter is intended to summarize the recent developments mostly based on the authors' works. It aims to provide a valuable reference for readers on the processing and analysis of vibration signals collected from rotating machinery.

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Section: Improvements For Eliminating End Effectsmentioning

confidence: 99%

Section: Improvements For Eliminating End Effectsmentioning

confidence: 99%

Section: Improvements For Eliminating End Effectsmentioning

confidence: 99%

Section: Adaptive Data Extension-based Spectral Coherencementioning

confidence: 99%

See 2 more Smart Citations

Adaptive Signal Decomposition Methods for Vibration Signals of Rotating Machinery

Guo¹,

Zuo²

2017

Fault Diagnosis and Detection

Self Cite

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show abstract

“…The complexity and stability of the signal are described more reasonably and effectively. The recognition of planetary gear status is important after extracting the feature information, and some intelligent classification technologies are widely used [7]. The MLP neural network has great practical value in pattern recognition in many fields, and it is composed of an input layer, an output layer and many hidden layers.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Planetary Gear Based on Fuzzy Entropy of CEEMDAN and MLP Neural Network by Using Vibration Signal

Chen

Liu

2017

ITM Web Conf.

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Abstract.A method of planetary gear fault diagnosis based on the fuzzy entropy of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-layer perceptron (MLP) neural network is proposed. The vibration signal is decomposed into multiple intrinsic mode functions (IMFs) by CEEMDAN, and the fuzzy entropy that combines the fuzzy function and sample entropy is proposed and used to extract the feature information contained in each IMF. The fuzzy entropies of each IMF are defined as the input of the MLP neural network, and the planetary gear status can be recognized by the output. The experiments prove the proposed method is effective.

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A Tutorial Review on Time-Frequency Analysis of Non-Stationary Vibration Signals with Nonlinear Dynamics Applications

et al. 2021

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Elimination of end effects in local mean decomposition using spectral coherence and applications for rotating machinery

Cited by 49 publications

References 24 publications

Adaptive Signal Decomposition Methods for Vibration Signals of Rotating Machinery

Adaptive Signal Decomposition Methods for Vibration Signals of Rotating Machinery

Fault Diagnosis of Planetary Gear Based on Fuzzy Entropy of CEEMDAN and MLP Neural Network by Using Vibration Signal

A Tutorial Review on Time-Frequency Analysis of Non-Stationary Vibration Signals with Nonlinear Dynamics Applications

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