scholarly journals Application of EMD-Based SVD and SVM to Coal-Gangue Interface Detection

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Wei Liu ◽  
Kai He ◽  
Qun Gao ◽  
Cheng-yin Liu
Keyword(s):  
Longwall Mining ◽  
Feature Vector ◽  
Vibration Signal ◽  
Singular Values ◽  
Coal Gangue ◽  
Mining Machine ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Vibration Signal Analysis

Coal-gangue interface detection during top-coal caving mining is a challenging problem. This paper proposes a new vibration signal analysis approach to detecting the coal-gangue interface based on singular value decomposition (SVD) techniques and support vector machines (SVMs). Due to the nonstationary characteristics in vibration signals of the tail boom support of the longwall mining machine in this complicated environment, the empirical mode decomposition (EMD) is used to decompose the raw vibration signals into a number of intrinsic mode functions (IMFs) by which the initial feature vector matrices can be formed automatically. By applying the SVD algorithm to the initial feature vector matrices, the singular values of matrices can be obtained and used as the input feature vectors of SVMs classifier. The analysis results of vibration signals from the tail boom support of a longwall mining machine show that the method based on EMD, SVD, and SVM is effective for coal-gangue interface detection even when the number of samples is small.

scholarly journals Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery

2009 ◽  
Vol 16 (1) ◽  
pp. 89-98 ◽  
Author(s):  
Junsheng Cheng ◽  
Dejie Yu ◽  
Jiashi Tang ◽  
Yu Yang
Keyword(s):  
Fault Diagnosis ◽  
Feature Vector ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Singular Values ◽  
Rotating Machinery ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition

Targeting the characteristics that periodic impulses usually occur whilst the rotating machinery exhibits local faults and the limitations of singular value decomposition (SVD) techniques, the SVD technique based on empirical mode decomposition (EMD) is applied to the fault feature extraction of the rotating machinery vibration signals. The EMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMFs) by which the initial feature vector matrices could be formed automatically. By applying the SVD technique to the initial feature vector matrices, the singular values of matrices could be obtained, which could be used as the fault feature vectors of support vector machines (SVMs) classifier. The analysis results from the gear and roller bearing vibration signals show that the fault diagnosis method based on EMD, SVD and SVM can extract fault features effectively and classify working conditions and fault patterns of gears and roller bearings accurately even when the number of samples is small.

Coal-gangue interface detection based on Hilbert spectral analysis of vibrations due to rock impacts on a longwall mining machine

2014 ◽  
Vol 229 (8) ◽  
pp. 1523-1531 ◽  
Author(s):  
Wei Liu ◽  
Kai He ◽  
Cheng-Yin Liu ◽  
Qun Gao ◽  
Yu-hua Yan
Keyword(s):  
Information Entropy ◽  
Mahalanobis Distance ◽  
Longwall Mining ◽  
Distance Measure ◽  
Coal Gangue ◽  
Mining Machine ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Hilbert Spectrum ◽  
Time Frequency

Coal-gangue interface detection during top-coal caving mining is a challenging problem. This paper proposes a new empirical approach to detect the coal-gangue interface based on vibration signal analysis of the tail boom support of the longwall mining machine. Due to nonstationary characteristics in vibration signals in this complicated environment, the empirical mode decomposition is used to decompose the original vibration signals into intrinsic mode functions. The associated Hilbert transform calculates the instantaneous frequency and amplitude of the selected intrinsic mode functions, providing a novel Hilbert spectrum in the time-frequency domain. The distribution of the Hilbert spectrum of top-coal caving is found to be more uniform than that of coal-gangue caving. A method of vibration feature extraction based on the information entropy of the Hilbert spectrum is presented. The Mahalanobis distance function is used to classify the caving states. Experimental results show that the Mahalanobis distance measure applied to the information entropy of the Hilbert spectrum of vibration signals from the tail boom support of a longwall mining machine is effective for coal-gangue interface detection.

A gearbox fault diagnosis method based on frequency-modulated empirical mode decomposition and support vector machine

2016 ◽  
Vol 232 (2) ◽  
pp. 369-380 ◽  
Author(s):  
Chao Zhang ◽  
Zhongxiao Peng ◽  
Shuai Chen ◽  
Zhixiong Li ◽  
Jianguo Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Dynamic State ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method

During the operation process of a gearbox, the vibration signals can reflect the dynamic states of the gearbox. The feature extraction of the vibration signal will directly influence the accuracy and effectiveness of fault diagnosis. One major challenge associated with the extraction process is the mode mixing, especially under such circumstance of intensive frequency. A novel fault diagnosis method based on frequency-modulated empirical mode decomposition is proposed in this paper. Firstly, several stationary intrinsic mode functions can be obtained after the initial vibration signal is processed using frequency-modulated empirical mode decomposition method. Using the method, the vibration signal feature can be extracted in unworkable region of the empirical mode decomposition. The method has the ability to separate such close frequency components, which overcomes the major drawback of the conventional methods. Numerical simulation results showed the validity of the developed signal processing method. Secondly, energy entropy was calculated to reflect the changes in vibration signals in relation to faults. At last, the energy distribution could serve as eigenvector of support vector machine to recognize the dynamic state and fault type of the gearbox. The analysis results from the gearbox signals demonstrate the effectiveness and veracity of the diagnosis approach.

scholarly journals Application of Feature Fusion Using Coaxial Vibration Signal for Diagnosis of Rolling Element Bearings

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jing Jiao ◽  
Jianhai Yue ◽  
Di Pei ◽  
Zhunqing Hu
Keyword(s):  
Fault Diagnosis ◽  
Feature Fusion ◽  
Random Noise ◽  
Vibration Signal ◽  
Fault Classification ◽  
Support Vector ◽  
Rolling Element Bearings ◽  
Intrinsic Mode Functions ◽  
Rolling Element ◽  
Vibration Signal Analysis

The research of rolling element bearings (REBs) fault diagnosis based on single sensor vibration signal analysis is very common. However, the information provided by an individual sensor is very limited, and the robustness of the system is poor. In this paper, a novel fault diagnosis method based on coaxial vibration signal feature fusion (CVSFF) is proposed to fully analyze the multisensor information of the system and build a more reliable diagnostic system. An ensemble empirical mode decomposition (EEMD) method is used to decompose the original vibration signal into a number of intrinsic mode functions (IMFs). Then the autocorrelation analysis is introduced to reduce the random noise remaining in IMFs. After that, the Rényi entropy is calculated as the feature of bearings. Finally, the features of coaxial vibration signal are fused by a multiple-kernel learning support vector machine (MKL-SVM) to classify bearing conditions. In order to verify the effectiveness of the CVSFF method in REB diagnosis, eight data sets from the Case Western Reserve University Bearing Data Center are selected. The fault classification results demonstrate that the proposed approach is a valuable tool for bearing faults detection, and the fused feature from coaxial sensors improves fault classification accuracy for REBs.

Vibration signal analysis of a hydropower unit based on adaptive local iterative filtering

2016 ◽  
Vol 231 (7) ◽  
pp. 1339-1353 ◽  
Author(s):  
Xueli An ◽  
Weiwei Yang ◽  
Xuemin An
Keyword(s):  
Vibration Signal ◽  
Hydraulic Turbine ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Nonstationary Vibration ◽  
Empirical Mode Decomposition Method ◽  
Vibration Signal Analysis ◽  
Iterative Filtering ◽  
Strong Vortex

The vibration signals coming from a hydropower unit have strong nonstationary characteristics when strong vortex develops in the hydraulic turbine draft tube. Related to this problem, a new vibration analysis method for a hydropower unit based on adaptive local iterative filtering is proposed. Firstly, adaptive local iterative filtering was used to decompose the complex vibration signal into several intrinsic mode functions. Then, frequency spectrum analysis of these components was performed to obtain the vortex characteristic frequency from the vibration signal. Simulated and real-world signals were used to verify the proposed method. The obtained results show that this method can overcome the problem of mode mixing in the existing empirical mode decomposition method, since it improves the efficiency and accuracy of feature extraction for nonstationary vibration signals from a hydropower unit.

scholarly journals Rope Tension Fault Diagnosis in Hoisting Systems Based on Vibration Signals Using EEMD, Improved Permutation Entropy, and PSO-SVM

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 209 ◽  
Author(s):  
Shaohua Xue ◽  
Jianping Tan ◽  
Lixiang Shi ◽  
Jiwei Deng
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Permutation Entropy ◽  
Support Vector ◽  
Sensitivity Index ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
New Perspective

Fault diagnosis of rope tension is significantly important for hoisting safety, especially in mine hoists. Conventional diagnosis methods based on force sensors face some challenges regarding sensor installation, data transmission, safety, and reliability in harsh mine environments. In this paper, a novel fault diagnosis method for rope tension based on the vibration signals of head sheaves is proposed. First, the vibration signal is decomposed into some intrinsic mode functions (IMFs) by the ensemble empirical mode decomposition (EEMD) method. Second, a sensitivity index is proposed to extract the main IMFs, then the de-noised signal is obtained by the sum of the main IMFs. Third, the energy and the proposed improved permutation entropy (IPE) values of the main IMFs and the de-noised signal are calculated to create the feature vectors. The IPE is proposed to improve the PE by adding the amplitude information, and it proved to be more sensitive in simulations of impulse detecting and signal segmentation. Fourth, vibration samples in different tension states are used to train a particle swarm optimization–support vector machine (PSO-SVM) model. Lastly, the trained model is implemented to detect tension faults in practice. Two experimental results validated the effectiveness of the proposed method to detect tension faults, such as overload, underload, and imbalance, in both single-rope and multi-rope hoists. This study provides a new perspective for detecting tension faults in hoisting systems.

scholarly journals Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Bo Wu ◽  
Zhe Tong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Optimal Number ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

Research on gearbox impact feature extraction method based on improved ESMD

2022 ◽  
Vol 64 (1) ◽  
pp. 20-27
Author(s):  
Fengfeng Bie ◽  
Sheng Gu ◽  
Yue Guo ◽  
Gang Yang ◽  
Jian Peng
Keyword(s):  
Feature Vector ◽  
Vibration Signal ◽  
Permutation Entropy ◽  
Significant Feature ◽  
Support Vector ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Envelope Spectrum ◽  
Impact Characteristics ◽  
The Impact

A gearbox vibration signal contains non-linear impact characteristics and the significant feature information tends to be overwhelmed by other interference components, which make it difficult to extract the typical fault features fully and effectively. Aiming at the key issue of how to effectively extract the impact characteristics, a fault diagnosis method based on improved extreme symmetric mode decomposition (ESMD) and a support vector machine (SVM) is proposed in this paper. The vibration signal is adaptively decomposed into multiple intrinsic mode function (IMF) components by the improved ESMD and then a certain number of components are selected with the maximum kurtosis-envelope spectrum index. The singular spectral entropy, energy entropy and permutation entropy of each component are applied to construct the feature vector set, in which the dimensionality of the set is reduced with the distance separability criterion. Finally, the dimension-reduced feature vector set is input into the SVM for pattern recognition. Dynamic simulation and experimental gearbox research show that the improved ESMD method can extract and identify gearbox fault information effectively.

scholarly journals A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3105 ◽  
Author(s):  
Cong Dai Nguyen ◽  
Alexander Prosvirin ◽  
Jong-Myon Kim
Keyword(s):  
Fault Diagnosis ◽  
Reference Signal ◽  
Vibration Signal ◽  
Support Vector ◽  
Health States ◽  
Informative Signal ◽  
Vibration Signals ◽  
Optimal Output ◽  
Gear Fault ◽  
Diagnosis Method

The vibration signals of gearbox gear fault signatures are informative components that can be used for gearbox fault diagnosis and early fault detection. However, the vibration signals are normally non-linear and non-stationary, and they contain background noise caused by data acquisition systems and the interference of other machine elements. Especially in conditions with varying rotational speeds, the informative components are blended with complex, unwanted components inside the vibration signal. Thus, to use the informative components from a vibration signal for gearbox fault diagnosis, the noise needs to be properly distilled from the informational signal as much as possible before analysis. This paper proposes a novel gearbox fault diagnosis method based on an adaptive noise reducer–based Gaussian reference signal (ANR-GRS) technique that can significantly reduce noise and improve classification from a one-against-one, multiclass support vector machine (OAOMCSVM) for the fault types of a gearbox. The ANR-GRS processes the shaft rotation speed to access and remove noise components in the narrowbands between two consecutive sideband frequencies along the frequency spectrum of a vibration signal, enabling the removal of enormous noise components with minimal distortion to the informative signal. The optimal output signal from the ANR-GRS is then extracted into many signal feature vectors to generate a qualified classification dataset. Finally, the OAOMCSVM classifies the health states of an experimental gearbox using the dataset of extracted features. The signal processing and classification paths are generated using the experimental testbed. The results indicate that the proposed method is reliable for fault diagnosis in a varying rotational speed gearbox system.

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