Coal and Rock Hardness Identification Based on EEMD and Multi-Scale Permutation Entropy

This study offers an efficient hardness identification approach to address the problem of poor real-time performance and accuracy in coal and rock hardness detection. To begin, Ensemble Empirical Mode Decomposition (EEMD) was performed on the current signal of the cutting motor to obtain a number of Intrinsic Mode Functions (IMFs). Further, the target signal was selected among the IMFs to reconstruct the current signal according to the energy density and correlation coefficient criteria. After that, the Multi-scale Permutation Entropy (MPE) of the reconstructed signal was trained by the Adaboost improved Back Propagation (BP) neural network, in order to establish the hardness recognition model. Finally, the cutting arm’s swing speed and the cutting head’s rotation speed were adjusted based on the coal and rock hardness. The simulation results indicated that using the energy density and correlation criterion to reconstruct the signal can successfully filter out noise interference. Compared to the BP model, the relative root-mean-square error of the Adaboost-BP model decreased by 0.0633, and the prediction results were more accurate. Additionally, the speed control strategy based on coal and rock hardness can ensure the efficient cutting of the roadheader.

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Gearbox Fault Diagnosis Using Complementary Ensemble Empirical Mode Decomposition and Permutation Entropy

Shock and Vibration ◽

10.1155/2016/3891429 ◽

2016 ◽

Vol 2016 ◽

pp. 1-8 ◽

Cited By ~ 14

Author(s):

Liye Zhao ◽

Wei Yu ◽

Ruqiang Yan

Keyword(s):

Fault Diagnosis ◽

Empirical Mode Decomposition ◽

Correlation Coefficients ◽

Ensemble Empirical Mode Decomposition ◽

Permutation Entropy ◽

Support Vector ◽

Svm Classifier ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Mode Decomposition

This paper presents an improved gearbox fault diagnosis approach by integrating complementary ensemble empirical mode decomposition (CEEMD) with permutation entropy (PE). The presented approach identifies faults appearing in a gearbox system based on PE values calculated from selected intrinsic mode functions (IMFs) of vibration signals decomposed by CEEMD. Specifically, CEEMD is first used to decompose vibration signals characterizing various defect severities into a series of IMFs. Then, filtered vibration signals are obtained from appropriate selection of IMFs, and correlation coefficients between the filtered signal and each IMF are used as the basis for useful IMFs selection. Subsequently, PE values of those selected IMFs are utilized as input features to a support vector machine (SVM) classifier for characterizing the defect severity of a gearbox. Case study conducted on a gearbox system indicates the effectiveness of the proposed approach for identifying the gearbox faults.

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Fault Diagnosis for Rolling Element Bearings Based on Feature Space Reconstruction and Multiscale Permutation Entropy

Entropy ◽

10.3390/e21050519 ◽

2019 ◽

Vol 21 (5) ◽

pp. 519 ◽

Cited By ~ 6

Author(s):

Weibo Zhang ◽

Jianzhong Zhou

Keyword(s):

Diagnostic Algorithm ◽

Feature Space ◽

Vibration Signal ◽

Rolling Bearing ◽

Ensemble Empirical Mode Decomposition ◽

Classification Performance ◽

Fault Classification ◽

Permutation Entropy ◽

Support Vector ◽

Intrinsic Mode Functions

Aimed at distinguishing different fault categories of severity of rolling bearings, a novel method based on feature space reconstruction and multiscale permutation entropy is proposed in the study. Firstly, the ensemble empirical mode decomposition algorithm (EEMD) was employed to adaptively decompose the vibration signal into multiple intrinsic mode functions (IMFs), and the representative IMFs which contained rich fault information were selected to reconstruct a feature vector space. Secondly, the multiscale permutation entropy (MPE) was used to calculate the complexity of reconstructed feature space. Finally, the value of multiscale permutation entropy was presented to a support vector machine for fault classification. The proposed diagnostic algorithm was applied to three groups of rolling bearing experiments. The experimental results indicate that the proposed method has better classification performance and robustness than other traditional methods.

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Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network

International Journal of Rotating Machinery ◽

10.1155/2017/9602650 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 14

Author(s):

Kai Chen ◽

Xin-Cong Zhou ◽

Jun-Qiang Fang ◽

Peng-fei Zheng ◽

Jun Wang

Keyword(s):

Fault Diagnosis ◽

Original Data ◽

Ensemble Empirical Mode Decomposition ◽

Intrinsic Mode Functions ◽

Mode Decomposition ◽

Coupling System ◽

Reconstructed Signal ◽

Gear Transmission System ◽

Gear Fault ◽

Diagnosis Method

A gear transmission system is a complex nonstationary and nonlinear time-varying coupling system. When faults occur on gear system, it is difficult to extract the fault feature. In this paper, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and Deep Briefs Network (DBN) is proposed to treat the vibration signals measured from gearbox. The original data is decomposed into a set of intrinsic mode functions (IMFs) using EEMD, and then main IMFs were chosen for reconstructed signal to suppress abnormal interference from noise. The reconstructed signals were regarded as input of DBN to identify gearbox working states and fault types. To verify the effectiveness of the EEMD-DBN in detecting the faults, a series of gear fault simulate experiments at different states were carried out. Results showed that the proposed method which coupled EEMD and DBN can improve the accuracy of gear fault identification and it is capable of applying to fault diagnosis in practical application.

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Application of Variational Mode Decomposition and Permutation Entropy for Rolling Bearing Fault Diagnosis

10.20855/ijav.2019.24.21325 ◽

2019 ◽

Vol 24 (2) ◽

pp. 303-311 ◽

Cited By ~ 5

Author(s):

Xiaoxia Zheng ◽

Guowang Zhou ◽

Dongdong Li ◽

Haohan Ren

Keyword(s):

Vibration Signal ◽

Rolling Bearing ◽

Permutation Entropy ◽

Support Vector ◽

Variational Mode Decomposition ◽

Intrinsic Mode Functions ◽

Bearing Fault Diagnosis ◽

Multi Scale ◽

Mode Decomposition ◽

Bearing Vibration

Rolling bearings are the key components of rotating machinery. However, the incipient fault characteristics of a rolling bearing vibration signal are weak and difficult to extract. To solve this problem, this paper presents a novel rolling bearing vibration signal fault feature extraction and fault pattern recognition method based on variational mode decomposition (VMD), permutation entropy (PE) and support vector machines (SVM). In the proposed method, the bearing vibration signal is decomposed by VMD, and the intrinsic mode functions (IMFs) are obtained in different scales. Then, the PE values of each IMF are calculated to uncover the multi-scale intrinsic characteristics of the vibration signal. Finally, PE values of IMFs are fed into SVM to automatically accomplish the bearing condition identifications. The proposed method is evaluated by rolling bearing vibration signals. The results indicate that the proposed method is superior and can diagnose rolling bearing faults accurately.

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QUANTITATIVE NON-STATIONARY ASSESSMENT OF CEREBRAL HEMODYNAMICS BY EMPIRICAL MODE DECOMPOSITION OF CEREBRAL DOPPLER FLOW VELOCITY

Advances in Adaptive Data Analysis ◽

10.1142/s1793536913500027 ◽

2013 ◽

Vol 05 (01) ◽

pp. 1350002 ◽

Cited By ~ 8

Author(s):

CHIA-CHI CHANG ◽

HUNG-YI HSU ◽

TZU-CHIEN HSIAO

Keyword(s):

Energy Density ◽

Flow Velocity ◽

Empirical Mode Decomposition ◽

Cerebral Blood Flow Velocity ◽

Ensemble Empirical Mode Decomposition ◽

Cerebral Hemodynamics ◽

Intrinsic Mode Functions ◽

Dynamic Regulation ◽

Arterial Blood ◽

Mode Decomposition

Dynamic regulation of cerebral circulation involves complex interaction between cardiovascular, respiratory, and autonomic nervous systems. Evaluating cerebral hemodynamics by using traditional statistic- and linear-based methods would underestimate or miss important information. Complementary ensemble empirical mode decomposition (CEEMD) has great capability of adaptive feature extraction from non-linear and non-stationary data without distortion. This study applied CEEMD for assessment of cerebral hemodynamics in response to physiologic challenges including paced 6-cycle breathing, hyperventilation, 7% CO2 breathing and head-up tilting test in twelve healthy subjects. Intrinsic mode functions (IMFs) were extracted from arterial blood pressure (ABP) and cerebral blood flow velocity (CBFV) signals, and was quantified by logarithmic averaged period and logarithmic energy density. The IMFs were able to show characteristics of ABP and CBFV waveform morphology in beat-to-beat timescale and in long-term trend scale. The changes in averaged period and energy density derived from IMFs were helpful for qualitative and quantitative assessment of ABP and CBFV responses to physiologic challenges. CEEMD is a promising method for assessing non-stationary components of systemic and cerebral hemodynamics.

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The Measurement and Elimination of Mode Splitting: From the Perspective of the Partly Ensemble Empirical Mode Decomposition

Complexity ◽

10.1155/2018/4230649 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Bin Liu ◽

Peng Zheng ◽

Qilin Dai ◽

Zhongli Zhou

Keyword(s):

Spectral Analysis ◽

White Noise ◽

Empirical Mode Decomposition ◽

Ensemble Empirical Mode Decomposition ◽

Permutation Entropy ◽

Intrinsic Mode Functions ◽

Mode Splitting ◽

Mode Decomposition ◽

Microseismic Signal ◽

Mode Mixing

The problems of mode mixing, mode splitting, and pseudocomponents caused by intermittence or white noise signals during empirical mode decomposition (EMD) are difficult to resolve. The partly ensemble EMD (PEEMD) method is introduced first. The PEEMD method can eliminate mode mixing via the permutation entropy (PE) of the intrinsic mode functions (IMFs). Then, bilateral permutation entropy (BPE) of the IMFs is proposed as a means to detect and eliminate mode splitting by means of the reconstructed signals in the PEEMD. Moreover, known ingredient component signals are comparatively designed to verify that the PEEMD method can effectively detect and progressively address the problem of mode splitting to some degree and generate IMFs with better performance. The microseismic signal is applied to prove, by means of spectral analysis, that this method is effective.

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Fault Diagnoise of Rolling Bearing Based on EEMD and Instantaneous Energy Density Spectrum

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.97-98.741 ◽

2011 ◽

Vol 97-98 ◽

pp. 741-744

Author(s):

Jin Ming Lu ◽

Fan Lin Meng ◽

Hua Shen ◽

Li Bing Ding ◽

Su Nin Bao

Keyword(s):

Energy Density ◽

Rolling Bearing ◽

Ensemble Empirical Mode Decomposition ◽

Intrinsic Mode Functions ◽

Density Spectrum ◽

Hilbert Spectrum ◽

Mode Decomposition ◽

Diagnosis Method ◽

Inner Race ◽

Instantaneous Energy

A new fault diagnosis method for rolling bearing based on ensemble empirical mode decomposition (EEMD) and instantaneous energy density spectrum is proposed here. The intrinsic mode functions (IMFs) generated by EEMD can alleviate the problem of mode mixing and approach the reality IMFs. The characteristic frequencies were found in the instantaneous energy density of Hilbert spectrum. The effectiveness of this method was demonstrated by analysis the vibration signals of a rolling bearing with inner-race fault.

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Application Research of a New Adaptive Noise Reduction Method in Fault Diagnosis

Applied Sciences ◽

10.3390/app10155078 ◽

2020 ◽

Vol 10 (15) ◽

pp. 5078

Author(s):

Wenxiao Guo ◽

Ruiqin Li ◽

Yanfei Kou ◽

Jianwei Zhang

Keyword(s):

Fault Diagnosis ◽

Fault Simulation ◽

Random Noise ◽

Vibration Signal ◽

Ensemble Empirical Mode Decomposition ◽

Intrinsic Mode Functions ◽

Time Frequency ◽

Mode Decomposition ◽

Reconstructed Signal ◽

Mining Machinery

The feature extraction of composite fault of gearbox in mining machinery has always been a difficulty in the field of fault diagnosis. Especially in strong background noise, the frequency of each fault feature is different, so an adaptive time-frequency analysis method is urgently needed to extract different types of faults. Considering that the signal after complementary ensemble empirical mode decomposition (CEEMD) contains a lot of pseudo components, which further leads to misdiagnosis. The article proposes a new method for actively removing noise components. Firstly, the best scale factor of multi-scale sample entropy (MSE) is determined by signals with different signal to noise ratios (SNRs); secondly, the minimum value of a large number of random noise MSE is extracted and used as the threshold of CEEMD; then, the effective Intrinsic mode functions(IMFs) component is reconstructed, and the reconstructed signal is CEEMD decomposed again; finally, after multiple iterations, the MSE values of the component signal that are less than the threshold are obtained, and the iteration is terminated. The proposed method is applied to the composite fault simulation signal and mining machinery vibration signal, and the composite fault feature is accurately extracted.

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A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

Entropy ◽

10.3390/e22030290 ◽

2020 ◽

Vol 22 (3) ◽

pp. 290

Author(s):

Jianghua Ge ◽

Tianyu Niu ◽

Di Xu ◽

Guibin Yin ◽

Yaping Wang

Keyword(s):

Fault Diagnosis ◽

High Frequency ◽

Signal Reconstruction ◽

Pearson Correlation ◽

Vibration Signal ◽

Rolling Bearing ◽

Support Vector ◽

Intrinsic Mode Functions ◽

Multi Scale ◽

Reconstructed Signal

Feature extraction is one of the challenging problems in fault diagnosis, and it has a direct bearing on the accuracy of fault diagnosis. Therefore, in this paper, a new method based on ensemble empirical mode decomposition (EEMD), wavelet semi-soft threshold (WSST) signal reconstruction, and multi-scale entropy (MSE) is proposed. First, the EEMD method is applied to decompose the vibration signal into intrinsic mode functions (IMFs), and then, the high-frequency IMFs, which contain more noise information, are screened by the Pearson correlation coefficient. Then, the WSST method is applied for denoising the high-frequency part of the signal to reconstruct the signal. Secondly, the MSE method is applied for calculating the MSE values of the reconstructed signal, to construct an eigenvector with the complexity measure. Finally, the eigenvector is input to a support vector machine (SVM) to find the fault diagnosis results. The experimental results prove that the proposed method, with a better classification performance, can better solve the problem of the effective signal and noise mixed in high-frequency signals. Based on the proposed method, the fault types can be accurately identified with an average classification accuracy of 100%.

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An Efficient Porcine Acoustic Signal Denoising Technique Based on EEMD-ICA-WTD

Mathematical Problems in Engineering ◽

10.1155/2019/2858740 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Sunan Zhang ◽

Jianyan Tian ◽

Amit Banerjee ◽

Jiangli Li

Keyword(s):

Acoustic Signal ◽

Correlation Coefficients ◽

Automatic Monitoring ◽

Acoustic Signals ◽

Ensemble Empirical Mode Decomposition ◽

Permutation Entropy ◽

Signal Denoising ◽

Intrinsic Mode Functions ◽

Independent Components ◽

Mode Decomposition

Automatic monitoring of group-housed pigs in real time through porcine acoustic signals has played a crucial role in automated farming. In the process of data collection and transmission, acoustic signals are generally interfered with noise. In this paper, an effective porcine acoustic signal denoising technique based on ensemble empirical mode decomposition (EEMD), independent component analysis (ICA), and wavelet threshold denoising (WTD) is proposed. Firstly, the porcine acoustic signal is decomposed into intrinsic mode functions (IMFs) by EEMD. In addition, permutation entropy (PE) is adopted to distinguish noise-dominant IMFs from the IMFs. Secondly, ICA is employed to extract the independent components (ICs) of the noise-dominant IMFs. The correlation coefficients of ICs and the first IMF are calculated to recognize noise ICs. The noise ICs will be removed. Then, WTD is applied to the other ICs. Finally, the porcine acoustic signal is reconstructed by the processed components. Experimental results show that the proposed method can effectively improve the denoising performance of porcine acoustic signal.

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