Convolutional plug-and-play sparse optimization for impulsive blind deconvolution

Mechanical Systems and Signal Processing - Tập 161 - Trang 107877 - 2021
Zhaohui Du1,2,3, Han Zhang4, Xuefeng Chen5, Yixin Yang1
1School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
2Key Laboratory of Ocean Acoustics and Sensing, Ministry of Industry and Information Technology, Xi’an 710072, China
3State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
4School of Construction Machinery, Chang’an University, Xi’an 710049, China
5State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, China

Tài liệu tham khảo

Du, 2020, Nonnegative bounded convolutional sparse learning method for envelope feature deconvolution, IEEE Trans. Instrum. Meas., 69, 8666, 10.1109/TIM.2020.2998564 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 Sawalhi, 2007, The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis, Mech. Syst. Signal Process., 21, 2616, 10.1016/j.ymssp.2006.12.002 He, 2016, Identification of multiple faults in rotating machinery based on minimum entropy deconvolution combined with spectral kurtosis, Mech. Syst. Signal Process., 81, 235, 10.1016/j.ymssp.2016.03.016 Wang, 2019, Minimum entropy deconvolution based on simulation-determined band pass filter to detect faults in axial piston pump bearings, ISA Trans., 88, 186, 10.1016/j.isatra.2018.11.040 Wang, 2019, Research and application of improved adaptive momeda fault diagnosis method, Measurement, 140, 63, 10.1016/j.measurement.2019.03.033 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 Zhang, 2019, Detection for weak fault in planetary gear trains based on an improved maximum correlation kurtosis deconvolution, Meas. Sci. Technol., 31, 10.1088/1361-6501/ab43ed Lyu, 2019, Application of improved mckd method based on qga in planetary gear compound fault diagnosis, Measurement, 139, 236, 10.1016/j.measurement.2019.02.071 Miao, 2020, Application of an improved mckda for fault detection of wind turbine gear based on encoder signal, Renew. Energy, 151, 192, 10.1016/j.renene.2019.11.012 McDonald, 2017, Multipoint optimal minimum entropy deconvolution and convolution fix: Application to vibration fault detection, Mech. Syst. Signal Process., 82, 461, 10.1016/j.ymssp.2016.05.036 Cheng, 2019, Adaptive multipoint optimal minimum entropy deconvolution adjusted and application to fault diagnosis of rolling element bearings, IEEE Sens. J., 1–1 Ma, 2019, Planet bearing fault diagnosis using multipoint Optimal Minimum Entropy Deconvolution Adjusted, J. Sound Vib., 449, 235, 10.1016/j.jsv.2019.02.024 Buzzoni, 2018, Blind deconvolution based on cyclostationarity maximization and its application to fault identification, J. Sound Vib., 432, 569, 10.1016/j.jsv.2018.06.055 Chen, 2020, Blind deconvolution assisted with periodicity detection techniques and its application to bearing fault feature enhancement, Measurement, 159, 10.1016/j.measurement.2020.107804 Y. X. H. Y. Wang, X., Weak fault detection for wind turbine bearing based on acycbd and iesb, J. Mech. Sci. Technol. 34 (2020) 1399–1413. Ovaclkll, 2016, Recovering periodic impulsive signals through skewness maximization, IEEE Trans. Signal Process., 64, 1586, 10.1109/TSP.2015.2502549 Pang, 2019, Weak fault diagnosis of rolling bearings based on singular spectrum decomposition, optimal lucy–richardson deconvolution and speed transform, Meas. Sci. Technol., 31, 10.1088/1361-6501/ab3ea3 Du, 2018, Convolutional sparse learning for blind deconvolution and application on impulsive feature detection, IEEE Trans. Instrum. Meas., 67, 338, 10.1109/TIM.2017.2777619 Had, 2019, A two-stage blind deconvolution strategy for bearing fault vibration signals, Mech. Syst. Signal Process., 134, 10.1016/j.ymssp.2019.106307 Cheng, 2019, A novel blind deconvolution method and its application to fault identification, J. Sound Vib., 460, 10.1016/j.jsv.2019.114900 Peeters, 2020, Blind filters based on envelope spectrum sparsity indicators for bearing and gear vibration-based condition monitoring, Mech. Syst. Signal Process., 138, 10.1016/j.ymssp.2019.106556 Jia, 2017, A geometrical investigation on the generalized lp/lq norm for blind deconvolution, Signal Process., 134, 63, 10.1016/j.sigpro.2016.11.018 Zhang, 2016, Kurtosis based weighted sparse model with convex optimization technique for bearing fault diagnosis, Mech. Syst. Signal Process., 80, 349, 10.1016/j.ymssp.2016.04.033 Zhang, 2020, Aero-engine bearing fault detection: a clustering low-rank approach, Mech. Syst. Signal Process., 138, 10.1016/j.ymssp.2019.106529 Cui, 2016, Double-dictionary matching pursuit for fault extent evaluation of rolling bearing based on the lempel-ziv complexity, J. Sound Vib., 385, 372, 10.1016/j.jsv.2016.09.008 Zhang, 2018, Bearing fault diagnosis using a whale optimization algorithm-optimized orthogonal matching pursuit with a combined time-frequency atom dictionary, Mech. Syst. Signal Process., 107, 29, 10.1016/j.ymssp.2018.01.027 Yang, 2018, Double-dictionary signal decomposition method based on split augmented lagrangian shrinkage algorithm and its application in gearbox hybrid faults diagnosis, J. Sound Vib., 432, 484, 10.1016/j.jsv.2018.06.064 Li, 2020, Multiple enhanced sparse decomposition for gearbox compound fault diagnosis, IEEE Trans. Instrum. Meas., 69, 770, 10.1109/TIM.2019.2905043 He, 2016, Sparsity-based algorithm for detecting faults in rotating machines, Mech. Syst. Signal Process., 72–73, 46, 10.1016/j.ymssp.2015.11.027 Qin, 2018, A new family of model-based impulsive wavelets and their sparse representation for rolling bearing fault diagnosis, IEEE Trans. Ind. Electron., 65, 2716, 10.1109/TIE.2017.2736510 Cai, 2018, Sparsity-enhanced signal decomposition via generalized minimax-concave penalty for gearbox fault diagnosis, J. Sound Vib., 432, 213, 10.1016/j.jsv.2018.06.037 Zhang, 2020, Collaborative sparse classification for aero-engine’s gear hub crack diagnosis, Mech. Syst. Signal Process., 141, 10.1016/j.ymssp.2019.106426 X. Chen, Z. Du, J. Li, X. Li, H. Zhang, Compressed sensing based on dictionary learning for extracting impulse components, Signal Process. 96, Part A (0) (2014) 94–109, time-frequency methods for condition based maintenance and modal analysis. Zhao, 2019, Enhanced sparse period-group lasso for bearing fault diagnosis, IEEE Trans. Ind. Electron., 66, 2143, 10.1109/TIE.2018.2838070 Wang, 2019, Synthesis versus analysis priors via generalized minimax-concave penalty for sparsity-assisted machinery fault diagnosis, Mech. Syst. Signal Process., 127, 202, 10.1016/j.ymssp.2019.02.053 Du, 2015, Sparse feature identification based on union of redundant dictionary for wind turbine gearbox fault diagnosis, IEEE Trans. Ind. Electron., 62, 6594, 10.1109/TIE.2015.2464297 Elad, 2010 Boyd, 2011, Distributed optimization and statistical learning via the alternating direction method of multipliers, Found. Trends Mach. Learn., 3, 1, 10.1561/2200000016 P. L. Combettes, J.-C. Pesquet, Proximal splitting methods in signal processing, in: Fixed-point algorithms for inverse problems in science and engineering, Springer, 2011, pp. 185–212. Davis, 2017, Faster convergence rates of relaxed peaceman-rachford and admm under regularity assumptions, Math. Oper. Res., 42, 783, 10.1287/moor.2016.0827 S. V. Venkatakrishnan, C. A. Bouman, B. Wohlberg, Plug-and-play priors for model based reconstruction, in: Proc. IEEE Global Conf. on Signal Inf. Process., 2013, pp. 945–948. S. Kay, A. Oppenheim, Fundamentals of Statistical Signal Processing, Volume II: Detection Theory, Prentice Hall, 1993. Dong, 2013, Nonlocally centralized sparse representation for image restoration, IEEE Trans. on Image Process., 22, 1620, 10.1109/TIP.2012.2235847 Fang, 2016, Super-resolution compressed sensing for line spectral estimation: an iterative reweighted approach, IEEE Trans. Signal Process., 64, 4649, 10.1109/TSP.2016.2572041 Wang, 2019, Global convergence of admm in nonconvex nonsmooth optimization, J. Scientific Comput., 78, 29, 10.1007/s10915-018-0757-z Wohlberg, 2016, Efficient algorithms for convolutional sparse representations, IEEE Trans. Image Process., 25, 301, 10.1109/TIP.2015.2495260 He, 2000, Alternating direction method with self-adaptive penalty parameters for monotone variational inequalities, Jour. Optim. Theory Appl., 106, 337, 10.1023/A:1004603514434 R. B. Randall, Vibration-based condition monitoring: industrial, aerospace and automotive applications, John Wiley &s066amp;)Sons, 2011. Chan, 2017, Plug-and-play admm for image restoration: fixed-point convergence and applications, IEEE Trans. Comput. Imaging., 3, 84, 10.1109/TCI.2016.2629286 Du, 2021, Low-rank enhanced convolutional sparse feature detection for accurate diagnosis of gearbox faults, Mech. Syst. Signal Process., 150, 10.1016/j.ymssp.2020.107215 Bishop, 2006 Fawcett, 2006, An introduction to roc analysis, Pattern Recognit. Lett., 27, 861, 10.1016/j.patrec.2005.10.010