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.

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 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.

A Modified EEMD Decomposition for the Detection of Rolling Bearing Faults

2014 ◽  
Vol 548-549 ◽  
pp. 369-373
Author(s):  
Yuan Cheng Shi ◽  
Yong Ying Jiang ◽  
Hai Feng Gao ◽  
Jia Wei Xiang
Keyword(s):  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Hilbert Spectrum ◽  
Bearing Faults ◽  
Mode Decomposition ◽  
Fault Features ◽  
Rolling Element ◽  
Inner Race

The vibration signals of rolling element bearings are non-linear and non-stationary and the corresponding fault features are difficult to be extracted. EEMD (Ensemble empirical mode decomposition) is effective to detect bearing faults. In the present investigation, MEEMD (Modified EEMD) is presented to diagnose the outer and inner race faults of bearings. The original vibration signals are analyzed using IMFs (intrinsic mode functions) extracted by MEEMD decomposition and Hilbert spectrum in the proposed method. The numerical and experimental results of the comparison between MEEMD and EEMD indicate that the proposed method is more effective to extract the fault features of outer and inner race of bearings than EEMD.

scholarly journals Planetary Gearbox Fault Diagnosis Based on ICEEMD-Time-Frequency Information Entropy and VPMCD

2020 ◽  
Vol 10 (18) ◽  
pp. 6376 ◽  
Author(s):  
Yihan Wang ◽  
Zhonghui Fan ◽  
Hongmei Liu ◽  
Xin Gao
Keyword(s):  
Fault Diagnosis ◽  
Information Entropy ◽  
Entropy Method ◽  
Characteristic Matrix ◽  
Intrinsic Mode Functions ◽  
Noise Component ◽  
Time Frequency ◽  
Frequency Information ◽  
Operation Conditions ◽  
Mode Decomposition

Planetary gearboxes are more and more widely used in large and complex construction machinery such as those used in aviation, aerospace fields, and so on. However, the movement of the gear is a typical complex motion and is often under variable conditions in real environments, which may make vibration signals of planetary gearboxes nonlinear and nonstationary. It is more difficult and complex to achieve fault diagnosis than to fix the axis gearboxes effectively. A fault diagnosis method for planetary gearboxes based on improved complementary ensemble empirical mode decomposition (ICEEMD)-time-frequency information entropy and variable predictive model-based class discriminate (VPMCD) is proposed in this paper. First, the vibration signal of planetary gearboxes is decomposed into several intrinsic mode functions (IMFs) by using the ICEEMD algorithm, which is used to determine the noise component by using the magnitude of the entropy and to remove the noise components. Then, the time-frequency information entropy of intrinsic modal function under the new decomposition is calculated and regarded as the characteristic matrix. Finally, the fault mode is classified by the VPMCD method. The experimental results demonstrate that the method proposed in this paper can not only solve the fault diagnosis of planetary gearboxes under different operation conditions, but can also be used for fault diagnosis under variable operation conditions. Simultaneously, the proposed method is superior to the wavelet entropy method and variational mode decomposition (VMD)-time-frequency information entropy.

HILBERT SPECTRUM IN TIME-FREQUENCY REPRESENTATION OF AUDIO SIGNALS CONSIDERING DISJOINT ORTHOGONALITY

2010 ◽  
Vol 02 (03) ◽  
pp. 313-336 ◽  
Author(s):  
MD. KHADEMUL ISLAM MOLLA ◽  
KEIKICHI HIROSE
Keyword(s):  
Hilbert Transform ◽  
Time Domain ◽  
Adaptive Method ◽  
Intrinsic Mode Functions ◽  
Audio Signals ◽  
Hilbert Spectrum ◽  
Time Frequency ◽  
Time Domain Signal ◽  
Frequency Representation ◽  
Non Stationary Signal

The performance of Hilbert spectrum (HS) in time-frequency representation (TFR) of audio signals is investigated in this paper. HS offers a fine-resolution TFR of time domain signals. It is derived by applying empirical mode decomposition (EMD), a newly developed data adaptive method for nonlinear and non-stationary signal analysis together with Hilbert transform. EMD represents any time domain signal as a sum of a finite number of bases called intrinsic mode functions (IMFs). The instantaneous frequency responses of the IMFs derived through Hilbert transform are arranged to obtain the TFR of the analyzing signal yielding the HS. The disjoint orthogonal property of audio signals is used as the decisive factor to measure the efficiency in TFR. Several audio signals are considered as disjoint orthogonal if not more than one source is active at any time-frequency cell. The performance of HS is compared with well known and widely used short-time Fourier transform technique for TFR. The experimental results show that HS based method performs better in time-frequency representation of the audio signals with the consideration of disjoint orthogonality.

scholarly journals ANALYSIS OF TEMPORAL VARIATIONS IN TURBIDITY FOR A COASTAL AREA USING THE HILBERT-HUANG-TRANSFORM

2011 ◽  
Vol 1 (32) ◽  
pp. 25
Author(s):  
Shigeru Kato ◽  
Magnus Larson ◽  
Takumi Okabe ◽  
Shin-ichi Aoki
Keyword(s):  
Instantaneous Frequency ◽  
River Mouth ◽  
Original Data ◽  
Temporal Variations ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
The Hilbert Transform

Turbidity data obtained by field observations off the Tenryu River mouth were analyzed using the Hilbert-Huang Transform (HHT) in order to investigate the characteristic variations in time and in the frequency domain. The Empirical Mode Decomposition (EMD) decomposed the original data into only eight intrinsic mode functions (IMFs) and a residue in the first step of the HHT. In the second step, the Hilbert transform was applied to the IMFs to calculate the Hilbert spectrum, which is the time-frequency distribution of the instantaneous frequency and energy. The changes in instantaneous frequencies showed correspondence to high turbidity events in the Hilbert spectrum. The investigation of instantaneous frequency variations can be used to understand transitions in the state of the turbidity. The comparison between the Fourier spectrum and the Hilbert spectrum integrated in time showed that the Hilbert spectrum makes it possible to detect and quantify the cycle of locally repeated events.

Looseness Diagnosis of Rotating Machinery Via Vibration Analysis Through Hilbert–Huang Transform Approach

2010 ◽  
Vol 132 (3) ◽  
Author(s):  
T. Y. Wu ◽  
Y. L. Chung ◽  
C. H. Liu
Keyword(s):  
Significance Test ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
Test Bed ◽  
Statistical Concept ◽  
Transform Method ◽  
Vibration Signals ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Fault Indicator

The objective of this research in this paper is to investigate the feasibility of utilizing the Hilbert–Huang transform method for diagnosing the looseness faults of rotating machinery. The complicated vibration signals of rotating machinery are decomposed into finite number of intrinsic mode functions (IMFs) by integrated ensemble empirical mode decomposition technique. Through the significance test, the information-contained IMFs are selected to form the neat time-frequency Hilbert spectra and the corresponding marginal Hilbert spectra. The looseness faults at different components of the rotating machinery can be diagnosed by measuring the similarities among the information-contained marginal Hilbert spectra. The fault indicator index is defined to measure the similarities among the information-contained marginal Hilbert spectra of vibration signals. By combining the statistical concept of Mahalanobis distance and cosine index, the fault indicator indices can render the similarities among the marginal Hilbert spectra to enhanced and distinguishable quantities. A test bed of rotor-bearing system is performed to illustrate the looseness faults at different mechanical components. The effectiveness of the proposed approach is evaluated by measuring the fault indicator indices among the marginal Hilbert spectra of different looseness types. The results show that the proposed diagnosis method is capable of classifying the distinction among the marginal Hilbert spectra distributions and thus identify the type of looseness fault at machinery.

A New Spectral Representation of Earthquake Recordings Using an Improved Hilbert-Huang Transform

2011 ◽  
Vol 255-260 ◽  
pp. 1671-1675
Author(s):  
Tian Li Huang ◽  
Wei Xin Ren ◽  
Meng Lin Lou
Keyword(s):  
Empirical Mode Decomposition ◽  
Spectral Representation ◽  
Low Frequency ◽  
Accurate Information ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Marginal Spectrum

A new spectral representation method of earthquake recordings using an improved Hilbert-Huang transform (HHT) is proposed in the paper. Firstly, the problem that the intrinsic mode functions (IMFs) decomposed by the empirical mode decomposition (EMD) in HHT is not exactly orthogonal is pointed out and improved through the Gram-Schmidt orthogonalization method which is referred as the orthogonal empirical mode decomposition (OEMD). Combined the OEMD and the Hilbert transform (HT) which is referred as the improved Hilbert-Huang transform (IHHT), the orthogonal intrinsic mode functions (OIMFs) and the orthogonal Hilbert spectrum (OHS) and the orthogonal Hilbert marginal spectrum (OHMS) are obtained. Then, the IHHT has been applied for the analysis of the El Centro earthquake recording. The obtained spectral representation result shows that the OHS gives more detailed and accurate information in a time–frequency–energy presentation than the Hilbert spectrum (HS) and the OHMS gives more faithful low-frequency energy presentation than the Fourier spectrum (FS) and the Hilbert marginal spectrum (HMS).

Hilbert-Huang Transform and Its Application in Gear Faults Diagnosis

2005 ◽  
Vol 291-292 ◽  
pp. 655-660 ◽  
Author(s):  
H. Li ◽  
H.Q. Zheng ◽  
L.W. Tang
Keyword(s):  
Signal Processing ◽  
Transient Analysis ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Vibration Signals ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Faults Diagnosis

Time-frequency and transient analysis have been widely used in signal processing and faults diagnosis. These methods represent important characteristics of a signal in both time and frequency domain. In this way, essential features of the signal can be viewed and analyzed in order to understand or model the faults characteristics. Historically, Fourier spectral analyses have provided a general approach for monitoring the global energy/frequency distribution. However, an assumption inherent to this method is the stationary and linear of the signal. As a result, Fourier methods are not generally an appropriate approach in the investigation of faults signals with transient components. This work presents the application of a new signal processing technique, empirical mode decomposition and the Hilbert spectrum, in analysis of vibration signals and gear faults diagnosis for a machine tool. The results show that this method may provide not only an increase in the spectral resolution but also reliability for the gear faults diagnosis.

Feature selection of the armature winding broken coils in synchronous motor using genetic algorithm and mahalanobis distance

2012 ◽  
Vol 57 (3) ◽  
pp. 829-835 ◽  
Author(s):  
Z. Głowacz ◽  
J. Kozik
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Mahalanobis Distance ◽  
Distance Measure ◽  
Synchronous Motor ◽  
Medical Diagnostics ◽  
Motor Current ◽  
Feature Spaces ◽  
Multidimensional Feature Spaces ◽  
Selection Of

The paper describes a procedure for automatic selection of symptoms accompanying the break in the synchronous motor armature winding coils. This procedure, called the feature selection, leads to choosing from a full set of features describing the problem, such a subset that would allow the best distinguishing between healthy and damaged states. As the features the spectra components amplitudes of the motor current signals were used. The full spectra of current signals are considered as the multidimensional feature spaces and their subspaces are tested. Particular subspaces are chosen with the aid of genetic algorithm and their goodness is tested using Mahalanobis distance measure. The algorithm searches for such a subspaces for which this distance is the greatest. The algorithm is very efficient and, as it was confirmed by research, leads to good results. The proposed technique is successfully applied in many other fields of science and technology, including medical diagnostics.

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