Wavelet threshold analysis combined with EMD method for mechanical equipment fault diagnosis

Wang, Lidong; Chen, Huanliang; Li, Shengye; Chen, Xuebo; Wang, Wei

doi:10.23919/chicc.2017.8028155

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Therefore, the ultimate goal of EMD decomposition is to find the component of the criticality. A continuous mean square error criterion is proposed in [10], shown as follows.…”

Section: Empirical Modal Decompositionmentioning

confidence: 99%

“…The main challenge is that most of the signals collected by the SUs are nonstationary and nonlinear. To cope with that, traditional noise reduction methods are proposed, including short time Fourier transform, Wigner Ville distribution, and wavelet transform [9,10]. However, these methods are all based on Fourier transforms, limited by the principle of uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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A Cooperative Spectrum Sensing Method Based on Empirical Mode Decomposition and Information Geometry in Complex Electromagnetic Environment

Wang

Zhang

et al. 2019

Complexity

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In a complex electromagnetic environment, there are cases where the noise is uncertain and difficult to estimate, which poses a great challenge to spectrum sensing systems. This paper proposes a cooperative spectrum sensing method based on empirical mode decomposition and information geometry. The method mainly includes two modules, a signal feature extraction module and a spectrum sensing module based on K-medoids. In the signal feature extraction module, firstly, the empirical modal decomposition algorithm is used to denoise the signals collected by the secondary users, so as to reduce the influence of the noise on the subsequent spectrum sensing process. Further, the spectrum sensing problem is considered as a signal detection problem. To analyze the problem more intuitively and simply, the signal after empirical mode decomposition is mapped into the statistical manifold by using the information geometry theory, so that the signal detection problem is transformed into geometric problems. Then, the corresponding geometric tools are used to extract signal features as statistical features. In the spectrum sensing module, the K-medoids clustering algorithm is used for training. A classifier can be obtained after a successful training, thereby avoiding the complex threshold derivation in traditional spectrum sensing methods. In the experimental part, we verified the proposed method and analyzed the experimental results, which show that the proposed method can improve the spectrum sensing performance.

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“…Therefore, the ultimate goal of EMD decomposition is to find the component of the criticality. A continuous mean square error criterion is proposed in [10], shown as follows.…”

Section: Empirical Modal Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Cooperative Spectrum Sensing Method Based on Empirical Mode Decomposition and Information Geometry in Complex Electromagnetic Environment

Wang

Zhang

et al. 2019

Complexity

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An integrated health condition detection method for rolling bearings using time-shift multi-scale amplitude-aware permutation entropy and uniform phase empirical mode decomposition

Gong

Yang

Feng

et al. 2021

Meas. Sci. Technol.

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Rolling bearings usually works in the condition with high speed, high temperature and high pressure, so their faults need to be detected and judged in time. This paper presents a new multi-scale entropy measurement algorithm called time-shift multi-scale amplitude-aware permutation entropy (TSMAAPE), combining time-shift coarse-grained method and multi-scale amplitude-aware permutation entropy (MAAPE) algorithm together creatively, making the extraction of useful information in time series more sufficient. In this paper, this new entropy algorithm is combined with uniform phase empirical mode decomposition algorithm, improved max-relevance and min-redundancy (ImRMR) algorithm and random forest algorithm, which are used to implement signal preprocessing, select features for dimensionality reduction, execute fault recognition and classification respectively, forming a new comprehensive detection method of bearing fault. The new comprehensive method includes two crucial parts: fault pre-detection and fault identification. Firstly, the threshold set based on TSMAAPE method is used to judge whether the bearing is in fault or not, if there is a fault, the subsequent steps are performed to identify and classify the fault. Compared with other traditional feature extraction methods, the proposed method is more effective and robust. The experimental results of two groups of bearing data show that the average accuracy of this method can reach 98.6%. The comprehensive detection method is of great significance for the rapid and accurate detection and identification of bearing faults in practical engineering applications.

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An Intelligent Selection Method Based On Electronic Component Quality Data System

Yang

Liu

et al. 2021

2021 2nd International Conference on Electronics, Communications and Information Technology (CECIT)

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Wavelet threshold analysis combined with EMD method for mechanical equipment fault diagnosis

Cited by 3 publications

References 7 publications

A Cooperative Spectrum Sensing Method Based on Empirical Mode Decomposition and Information Geometry in Complex Electromagnetic Environment

A Cooperative Spectrum Sensing Method Based on Empirical Mode Decomposition and Information Geometry in Complex Electromagnetic Environment

An integrated health condition detection method for rolling bearings using time-shift multi-scale amplitude-aware permutation entropy and uniform phase empirical mode decomposition

An Intelligent Selection Method Based On Electronic Component Quality Data System

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