Optimal swarm decomposition with whale optimization algorithm for weak feature extraction from multicomponent modulation signal
Tài liệu tham khảo
Randall, 2011, Rolling element bearing diagnostics—a tutorial, Mech. Syst. Signal Process., 25, 485, 10.1016/j.ymssp.2010.07.017
Peng, 2004, Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography, Mech. Syst. Signal Process., 18, 199, 10.1016/S0888-3270(03)00075-X
Miao, 2016, Sparse maximum harmonics-to-noise-ratio deconvolution for weak fault signature detection in bearings, Meas. Sci. Technol., 27, 10.1088/0957-0233/27/10/105004
Jia, 2016, Investigation on the kurtosis filter and the derivation of convolutional sparse filter for impulsive signature enhancement, J. Sound Vib., 386, 433, 10.1016/j.jsv.2016.10.005
Antoni, 2006, The spectral kurtosis: a useful tool for characterising non-stationary signals, Mech. Syst. Signal Process., 20, 282, 10.1016/j.ymssp.2004.09.001
Wang, 2011, An adaptive SK technique and its application for fault detection of rolling element bearings, Mech. Syst. Signal Process., 25, 1750, 10.1016/j.ymssp.2010.12.008
Antoni, 2007, Fast computation of the kurtogram for the detection of transient faults, Mech. Syst. Signal Process., 21, 108, 10.1016/j.ymssp.2005.12.002
Peter, 2013, The design of a new sparsogram for fast bearing fault diagnosis: part 1 of the two related manuscripts that have a joint title as “Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement–Parts 1 and 2”, Mech. Syst. Signal Process., 40, 499, 10.1016/j.ymssp.2013.05.024
Antoni, 2016, The infogram: entropic evidence of the signature of repetitive transients, Mech. Syst. Signal Process., 74, 73, 10.1016/j.ymssp.2015.04.034
Wang, 2016, A new SKRgram based demodulation technique for planet bearing fault detection, J. Sound Vib., 385, 330, 10.1016/j.jsv.2016.08.026
Miao, 2019, Periodicity-impulsiveness spectrum based on singular value negentropy and its application for identification of optimal frequency band, IEEE Trans. Ind. Electron., 66, 3127, 10.1109/TIE.2018.2844792
Huang, 1998, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. Lond. A, 5, 903, 10.1098/rspa.1998.0193
Lei, 2013, A review on empirical mode decomposition in fault diagnosis of rotating machinery, Mech. Syst. Signal Process., 35, 108, 10.1016/j.ymssp.2012.09.015
Wu, 2009, Ensemble empirical mode decomposition: a noise-assisted data analysis method, Adv. Adaptive Data Anal., 1, 1, 10.1142/S1793536909000047
Jiang, 2014, Fault identification of rotor-bearing system based on ensemble empirical mode decomposition and self-zero space projection analysis, J. Sound Vib., 333, 3321, 10.1016/j.jsv.2014.03.014
Xue, 2015, An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis, Mech. Syst. Signal Process., 62, 444, 10.1016/j.ymssp.2015.03.002
Li, 2017, Application of bandwidth EMD and adaptive multi-scale morphology analysis for incipient fault diagnosis of rolling bearings, IEEE Trans. Ind. Electron., 64, 6506, 10.1109/TIE.2017.2650873
Wang, 2018, Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings, Mech. Syst. Signal Process., 101, 292, 10.1016/j.ymssp.2017.08.038
Chen, 2016, Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals, Renew. Energy, 89, 80, 10.1016/j.renene.2015.12.010
Pan, 2016, Mono-component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data-driven adaptive Fourier spectrum segment, Mech. Syst. Signal Process., 72, 160, 10.1016/j.ymssp.2015.10.017
Dragomiretskiy, 2014, Variational mode decomposition, Signal Process. IEEE Trans., 62, 531, 10.1109/TSP.2013.2288675
Wang, 2015, Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system, Mech. Syst. Signal Process., 60, 243, 10.1016/j.ymssp.2015.02.020
Miao, 2017, Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings, Mech. Syst. Signal Process., 92, 173, 10.1016/j.ymssp.2017.01.033
Wang, 2018, Time–frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis, Mech. Syst. Signal Process., 103, 60, 10.1016/j.ymssp.2017.09.042
Liu, 2016, Variational mode decomposition denoising combined the detrended fluctuation analysis, Signal Process., 125, 349, 10.1016/j.sigpro.2016.02.011
Mirjalili, 2016, The whale optimization algorithm, Adv. Eng. Softw., 95, 51, 10.1016/j.advengsoft.2016.01.008
Dao, 2016, A multi-objective optimal mobile robot path planning based on whale optimization algorithm, 337
Ahmed, 2017, Maximizing lifetime of wireless sensor networks based on whale optimization algorithm, 724
Apostolidis, 2017, Swarm decomposition: a novel signal analysis using swarm intelligence, Signal Process., 132, 40, 10.1016/j.sigpro.2016.09.004
Antoni, 2017, Fast computation of the spectral correlation, Mech. Syst. Signal Process., 92, 248, 10.1016/j.ymssp.2017.01.011
Xu, 2017, Detecting weak position fluctuations from encoder signal using singular spectrum analysis, ISA Trans., 71, 440, 10.1016/j.isatra.2017.09.001
Zhao, 2017, Health assessment of rotating machinery using a rotary encoder, IEEE Trans. Ind. Electron., 65, 2548, 10.1109/TIE.2017.2739689
Zhao, 2018, Instantaneous speed jitter detection via encoder signal and its application for the diagnosis of planetary gearbox, Mech. Syst. Signal Process., 98, 16, 10.1016/j.ymssp.2017.04.033
Zhao, 2018, Health assessment of rotating machinery using a rotary encoder, IEEE Trans. Ind. Electron., 65, 2548, 10.1109/TIE.2017.2739689
Jiao, 2018, A multivariate encoder information based convolutional neural network for intelligent fault diagnosis of planetary gearboxes, Knowl.-Based Syst., 160, 237, 10.1016/j.knosys.2018.07.017
Xu, 2018, Repetitive transient extraction for machinery fault diagnosis using multiscale fractional order entropy infogram, Mech. Syst. Signal Process., 103, 312, 10.1016/j.ymssp.2017.10.024
McDonald, 2012, Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection, Mech. Syst. Signal Process., 33, 237, 10.1016/j.ymssp.2012.06.010
Xu, 2016, Envelope harmonic-to-noise ratio for periodic impulses detection and its application to bearing diagnosis, Measurement, 91, 385, 10.1016/j.measurement.2016.05.073
Endo, 2007, Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter, Mech. Syst. Signal Process., 21, 906, 10.1016/j.ymssp.2006.02.005
Jia, 2018, Sparse filtering with the generalized lp/lq norm and its applications to the condition monitoring of rotating machinery, Mech. Syst. Signal Process., 102, 198, 10.1016/j.ymssp.2017.09.018
Miao, 2017, Improvement of kurtosis-guided-grams via Gini index for bearing fault feature identification, Meas. Sci. Technol., 28, 10.1088/1361-6501/aa8a57
Barszcz, 2011, A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram, Mech. Syst. Signal Process., 25, 431, 10.1016/j.ymssp.2010.05.018
Singh, 2015, An extensive review of vibration modelling of rolling element bearings with localised and extended defects, J. Sound Vib., 357, 300, 10.1016/j.jsv.2015.04.037
Hong, 2014, In situ health monitoring for bogie systems of CRH380 train on Beijing-Shanghai high-speed railway, Mech. Syst. Signal Process., 45, 378, 10.1016/j.ymssp.2013.11.017