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Sun J.,Yanshan University | Li Y.,Yanshan University | Wen J.,Key Laboratory of Measurement Technology and Instrumentation of Hebei Province | Yan S.,Yanshan University
Neurocomputing | Year: 2015

This paper proposes the use of density-based spatial clustering of application with noise (DBSCAN) and the Hough transform to estimate the mixing matrix in underdetermined blind source separation. First, phase-angle-based single source time-frequency point detection is employed to improve signal sparsity. To overcome the limitation of the K-means clustering algorithm, which requires prior knowledge of the number of sources, the DBSCAN classification algorithm is adopted to automatically estimate the number of sources and then further estimate the mixing matrix. The Hough transform is employed to modify the cluster center in order to enhance the estimation accuracy of the mixing matrix. Simulation results show that the proposed approach can effectively estimate the number of sources and the mixing matrix with high accuracy. The proposed approach performs better than the K-means method and the DBSCAN algorithm alone. © 2015 Elsevier B.V.


Meng Z.,Yanshan University | Meng Z.,Key Laboratory of Measurement Technology and Instrumentation of Hebei Province | Liang Z.,Yanshan University | Liang Z.,Guangxi Special Equipment Supervision and Inspection Institute
Jiliang Xuebao/Acta Metrologica Sinica | Year: 2015

The accurate AR model can reveal the changing state characteristics inherent in the signal, however the AR model is sensitive to the changes in the state of the system, and the multiple of dynamic source signal coupling is bound to affect the estimated results. The method of blind source separation is reconstruct mechanical vibration source signals. Then the non-stationary fault signal is decomposed into several stationary signals which suit to establish AR model. Finally, the AR model of stationary intrinsic mode function is constructed to extract the characteristics of fault vibration signal. The results of simulation and experiment are presented to verify the theory analysis. ©, 2015, Chinese Society for Measurement. All right reserved.


Meng Z.,Key Laboratory of Measurement Technology and Instrumentation of Hebei Province | Meng Z.,National Engineering Research Center for Equipment & Technology of Cold Rolling Strip | Yan X.-L.,Key Laboratory of Measurement Technology and Instrumentation of Hebei Province
Jiliang Xuebao/Acta Metrologica Sinica | Year: 2015

Based on the differential-based empirical mode decomposition (DEMD) and hidden Markov model (HMM), a new method for rotating machinery fault diagnosis is proposed. The method is applied to rolling bearing fault diagnosis. First of all, fault signals are decomposed by DEMD, the instantaneous energy distribution of each signal is extracted to form the fault feature vectors, and then input the feature vectors into the HMM classifier for malfunction recognition, the maximum likelihood probability which is output by HMM classifier is in the fault state. Finally, different fault types are recognized. A practical fault signal of a rolling bearing with corrosive pitting is applied to test the method. Experimental result showed that the method of DEMD-HMM is superior to the method of EMD-HMM and can identify the rolling bearing fault accurately and effectively. © 2015, Chinese Society for Measurement. All right reserved.


Meng Z.,Key Laboratory of Measurement Technology and Instrumentation of Hebei Province | Meng Z.,National Engineering Research Center for Eqpt & Technology of Cold Rolling Strip | Ji Y.,Key Laboratory of Measurement Technology and Instrumentation of Hebei Province | Yan X.-L.,Key Laboratory of Measurement Technology and Instrumentation of Hebei Province
Jiliang Xuebao/Acta Metrologica Sinica | Year: 2016

A comprehensive rolling bearing fault diagnosis method combining differential-based empirical mode decomposition (DEMD) with fuzzy entropy and support vector machine (SVM) is proposed. Firstly, mechanical vibration signal is decomposed with differential-based empirical mode decomposition (DEMD) to obtain a certain number of intrinsic mode functions (IMFs) that have physical meaning. With a mutual relationship rule, the IMF components that have largest correlation coefficients with the original signal are sifted out. The fuzzy entropies of these IMFs are calculated and use as eigenvectors of fault signals, then the eigenvectors are put into SVM to identify the state of the rolling bearing. Compared with the method based on empirical mode decomposition (EMD) combined with fuzzy entropy and SVM, the experimental results show that the method of mechanical failure signals can accurately identify classification effectively. © 2016, Acta Metrologica Sinica Press. All right reserved.


Zhang Y.-Y.,Yanshan University | Zhang Y.-Y.,Key Laboratory of Measurement Technology and Instrumentation of Hebei Province | Zhou H.,Yanshan University | Zhou H.,Key Laboratory of Measurement Technology and Instrumentation of Hebei Province | And 2 more authors.
Wuli Xuebao/Acta Physica Sinica | Year: 2015

In order to achieve precise extraction of the phase with a light feedback mechanism, based on empirical mode decomposition (EMD) algorithm, an adaptive phase extraction method is proposed in this paper. First of all, the EMD algorithm is acted on the self-mixing interference (SMI) mixed noise signals, then using the principle of HHT to extract the instantaneous phase information in the SMI signal in time and retrieve the true phase of the object from the wrapped phase. In this paper, the phase extraction algorithm based on EMD are simulated under different optical feedback conditions. Finally, an experimental setup based on SMI has been given for demonstration. Experimental results show that this method is correct in principle and can be used in the precise extraction and its maximum error is less than 1.6 rad. The simulation results are consistent with the experimental data, which show the effectiveness of the proposed method. ©, 2015, Institute of Physics, Chinese Academy of Sciences. All right reserved.

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