An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing

Li, Meijiao; Wang, Huaqing; Tang, Gang; Yuan, Hairong; Yang, Yang

doi:10.1155/2014/676205

Cited by 34 publications

(24 citation statements)

References 24 publications

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“…Colominas et al proposed a new improved CEEMD; the new method was used for artificial signals and real biomedical signals [19]. The improvements of the CEEMD method have been achieved through the application of correlation theory [20][21][22][23][24]. The main steps of the CEEMD are as follows:…”

Section: The Proposed Methodsmentioning

confidence: 99%

Noncontact Measurement and Detection of Instantaneous Seismic Attributes Based on Complementary Ensemble Empirical Mode Decomposition

Huang

Malekian

et al. 2017

Energies

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Hilbert-Huang transform (HHT) is a popular method to analyze nonlinear and non-stationary data. It has been widely used in geophysical prospecting. This paper analyzes the mode mixing problems of empirical mode decomposition (EMD) and introduces the noncontact measurement and detection of instantaneous seismic attributes using complementary ensemble empirical mode decomposition (CEEMD). Numerical simulation testing indicates that the CEEMD can effectively solve the mode mixing problems of EMD and can provide stronger anti-noise ability. The decomposed results of the synthetic seismic record show that CEEMD has a better ability to decompose seismic signals. Then, CEEMD is applied to extract instantaneous seismic attributes of 3D seismic data in a real-world coal mine in Inner Mongolia, China. The detection results demonstrate that instantaneous seismic attributes extracted by CEEMD are helpful to effectively identify the undulations of the top interfaces of limestone.

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Section: The Proposed Methodsmentioning

confidence: 99%

Noncontact Measurement and Detection of Instantaneous Seismic Attributes Based on Complementary Ensemble Empirical Mode Decomposition

Huang

Malekian

et al. 2017

Energies

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“…Guo et al [71] proposed an enhanced EEMD, which used the similarity criterion to generate the monocomponent for accurate fault diagnosis of rolling bearings. References [72] and [73] introduced a noise-improved method called complementary EEMD (CEEMD) to detect faults at the early stage of degradation. In this method, a cross-correlation coefficient was used to reduce noise and select effective IMFs.…”

Section: Eemdmentioning

confidence: 99%

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

Wei

et al. 2019

Entropy

170

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Rotating machinery is widely applied in various types of industrial applications. As a promising field for reliability of modern industrial systems, early fault diagnosis (EFD) techniques have attracted increasing attention from both academia and industry. EFD is critical to provide appropriate information for taking necessary maintenance actions and thereby prevent severe failures and reduce financial losses. A massive amounts of research work has been conducted in last two decades to develop EFD techniques. This paper reviews and summarizes the research works on EFD of gears, rotors, and bearings. The main purpose of this paper is to serve as a guidemap for researchers in the field of early fault diagnosis. After a brief introduction of early fault diagnosis techniques, the applications of EFD of rotating machine are reviewed in two aspects: fault frequency-based methods and artificial intelligence-based methods. Finally, a summary and some new research prospects are discussed.

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“…. EMD was put forward by Huang N. E. in 1998, which seemed much more promising and had appealed a lot of attention [14]. e EMD algorithm can decompose a nonlinear signal self-adaptively into some IMFs and a residual series [15]:…”

Section: Empirical Mode Decompositionmentioning

confidence: 99%

A Rolling Bearing Fault Diagnosis Method Based on EMD and Quantile Permutation Entropy

Qiangqiang¹,

Dai²,

Dai³

2019

Mathematical Problems in Engineering

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The vibration signals resulting from rolling bearings are nonlinear and nonstationary, and an approach for the fault diagnosis of rolling bearings using the quantile permutation entropy and EMD (empirical mode decomposition) is proposed. Firstly, the EMD is used to decompose the rolling bearings vibration signal, and several IMFs (intrinsic mode functions) spanning different scales are obtained. Secondly, aiming at the shortcomings of the permutation entropy algorithm, a new permutation entropy algorithm based on sample quantile is proposed, and the quantile permutation entropy of the first few IMFs, which contain the main fault information, is calculated. The quantile permutation entropies are accordingly seen as the characteristic vector and then input to the particle swarm optimization and support vector machine. Finally, the proposed method is applied to the experimental data. The analysis results show that the proposed approach can effectively achieve fault diagnosis of rolling bearings.

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An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing

Cited by 34 publications

References 24 publications

Noncontact Measurement and Detection of Instantaneous Seismic Attributes Based on Complementary Ensemble Empirical Mode Decomposition

Noncontact Measurement and Detection of Instantaneous Seismic Attributes Based on Complementary Ensemble Empirical Mode Decomposition

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

A Rolling Bearing Fault Diagnosis Method Based on EMD and Quantile Permutation Entropy

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