Basic research on machinery fault diagnostics: Past, present, and future trends

Xuefeng CHEN , Shibin WANG , Baijie QIAO , Qiang CHEN

Front. Mech. Eng. ›› 2018, Vol. 13 ›› Issue (2) : 264 -291.

PDF (875KB)
Front. Mech. Eng. ›› 2018, Vol. 13 ›› Issue (2) : 264 -291. DOI: 10.1007/s11465-018-0472-3
REVIEW ARTICLE
REVIEW ARTICLE

Basic research on machinery fault diagnostics: Past, present, and future trends

Author information +
History +
PDF (875KB)

Abstract

Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.

Keywords

fault diagnosis / fault mechanism / feature extraction / signal processing / intelligent diagnostics

Cite this article

Download citation ▾
Xuefeng CHEN, Shibin WANG, Baijie QIAO, Qiang CHEN. Basic research on machinery fault diagnostics: Past, present, and future trends. Front. Mech. Eng., 2018, 13(2): 264-291 DOI:10.1007/s11465-018-0472-3

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Roy R, Stark R, Tracht K, Continuous maintenance and the future—Foundations and technological challenges. CIRP Annals-Manufacturing Technology, 2016, 65(2): 667–688

[2]

Yan R, Gao R X. Wavelet transform: A mathematical tool for non-stationary signal processing in measurement science Part 2 in a series of tutorials in instrumentation and measurement. IEEE Instrumentation & Measurement Magazine, 2009, 12(5): 35–44

[3]

Lewicki D G, Decker H J, Shimski J T.Development of a full-scale transmission testing procedure to evaluate advanced lubricants. NASA Technical Report 92N30396. 1992

[4]

Samuel P D, Pines D J. A review of vibration-based techniques for helicopter transmission diagnostics. Journal of Sound and Vibration, 2005, 282(1–2): 475–508

[5]

Chen B, Zhang Z, Zi Y, Detecting of transient vibration signatures using an improved fast spatial-spectral ensemble kurtosis kurtogram and its applications to mechanical signature analysis of short duration data from rotating machinery. Mechani cal Systems and Signal Processing, 2013, 40(1): 1–37

[6]

Cai G, Chen X, He Z. Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox. Mechanical Systems and Signal Processing, 2013, 41(1–2): 34–53

[7]

McGarry B. Pratt & Whitney: F-35 fleet will have engine fix by early 2016. 2015. Retrieved form

[8]

Clark C. JSF fire looks like ‘Isolated Event’; F-35A stay on ground. 2014. Retrieved form

[9]

Clark C. F-35 head bogdan explains the F135 ‘Bad Rub’ Fix. 2014. Retrieved form

[10]

Devendiran S, Manivannan K. Vibration based condition monitoring and fault diagnosis technologies for bearing and gear components—A review. International Journal of Applied Engineering Research, 2016, 11(6): 3966–3975

[11]

Desbazeille M, Randall R B, Guillet F, Model-based diagnosis of large diesel engines based on angular speed variations of the crankshaft. Mechanical Systems and Signal Processing, 2010, 24(5): 1529–1541

[12]

Gupta P. Dynamics of rolling-element bearings—Part I: Cylindrical roller bearing analysis. Journal of Lubrication Technology, 1979, 101(3): 293–302

[13]

Gupta P K. Dynamics of rolling-element bearings. 1. Cylindrical roller bearing analysis. Journal of lubrication technology, 1979, 101: 293–304

[14]

Gupta P K. Dynamics of rolling-element bearings. 2. Cylindrical roller bearing results. Journal of lubrication technology, 1979, 101: 305–311

[15]

Gupta P K. Dynamics of rolling-element bearings. 3. Ball bearing analysis. Journal of lubrication technology, 1979, 101: 312–318

[16]

Gupta P K. Dynamics of rolling-element bearings. 4. Ball bearing results. Journal of lubrication technology, 1979, 101: 319–326

[17]

Gupta P K.Advanced Dynamics of Rolling Elements. New York: Springer, 1984

[18]

Niu L, Cao H, He Z, Dynamic modeling and vibration response simulation for high speed rolling ball bearings with localized surface defects in raceways. Journal of Manufacturing Science and Engineering, 2014, 136(4): 041015

[19]

Wang F, Jing M, Yi J, Dynamic modelling for vibration analysis of a cylindrical roller bearing due to localized defects on raceways. Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics, 2015, 229(1): 39–64

[20]

Moazen Ahmadi A, Petersen D, Howard C. A nonlinear dynamic vibration model of defective bearings—The importance of modelling the finite size of rolling elements. Mechanical Systems and Signal Processing, 2015, 52–53: 309–326

[21]

Niu L, Cao H, He Z, A systematic study of ball passing frequencies based on dynamic modeling of rolling ball bearings with localized surface defects. Journal of Sound and Vibration, 2015, 357: 207–232

[22]

Patel V, Tandon N, Pandey R. A dynamic model for vibration studies of deep groove ball bearings considering single and multiple defects in races. Journal of Tribology, 2010, 132(4): 041101

[23]

Ma H, Pang X, Feng R, Fault features analysis of cracked gear considering the effects of the extended tooth contact. Engineering Failure Analysis, 2015, 48: 105–120

[24]

Ma H, Song R, Pang X, Time-varying mesh stiffness calculation of cracked spur gears. Engineering Failure Analysis, 2014, 44: 179–194

[25]

Dimarogonas A D. Vibration of cracked structures: A state of the art review. Engineering Fracture Mechanics, 1996, 55(5): 831–857

[26]

Wauer J. On the dynamics of cracked rotors: A literature survey. Applied Mechanics Reviews, 1990, 43(1): 13–17

[27]

Gasch R. A survey of the dynamic behaviour of a simple rotating shaft with a transverse crack. Journal of Sound and Vibration, 1993, 160(2): 313–332

[28]

Bachschmid N, Pennacchi P. Crack effects in rotor dynamics. Mechanical Systems and Signal Processing, 2008, 22(4): 761–762

[29]

Pennacchi P, Bachschmid N, Vania A. A model-based identification method of transverse cracks in rotating shafts suitable for industrial machines. Mechanical Systems and Signal Processing, 2006, 20(8): 2112–2147

[30]

Papadopoulos C A. The strain energy release approach for modeling cracks in rotors: A state of the art review. Mechanical Systems and Signal Processing, 2008, 22(4): 763–789

[31]

Sekhar A. Multiple cracks effects and identification. Mechanical Systems and Signal Processing, 2008, 22(4): 845–878

[32]

Gasch R. Dynamic behaviour of the Laval rotor with a transverse crack. Mechanical Systems and Signal Processing, 2008, 22(4): 790–804

[33]

Pennacchi P, Bachschmid N, Vania A, Use of modal representation for the supporting structure in model-based fault identification of large rotating machinery: Part 1—Theoretical remarks. Mechanical Systems and Signal Processing, 2006, 20(3): 662–681

[34]

Pennacchi P, Vania A, Bachschmid N. Increasing the robustness of fault identification in rotor dynamics by means of M-estimators. Mechanical Systems and Signal Processing, 2007, 21(8): 3003–3029

[35]

Simani S, Fantuzzi C. Dynamic system identification and model-based fault diagnosis of an industrial gas turbine prototype. Mechatronics, 2006, 16(6): 341–363

[36]

Simani S, Patton R J. Fault diagnosis of an industrial gas turbine prototype using a system identification approach. Control Engineering Practice, 2008, 16(7): 769–786

[37]

Hou L, Chen Y, Cao Q, Nonlinear vibration analysis of a cracked rotor-ball bearing system during flight maneuvers. Mechanism and Machine Theory, 2016, 105: 515–528

[38]

Lu K, Jin Y, Chen Y, Stability analysis of reduced rotor pedestal looseness fault model. Nonlinear Dynamics, 2015, 82(4): 1611–1622

[39]

Liang X, Zuo M J, Hoseini M R. Vibration signal modeling of a planetary gear set for tooth crack detection. Engineering Failure Analysis, 2015, 48: 185–200

[40]

Ma H, Tai X, Han Q, A revised model for rubbing between rotating blade and elastic casing. Journal of Sound and Vibration, 2015, 337: 301–320

[41]

Ma H, Zeng J, Feng R, Review on dynamics of cracked gear systems. Engineering Failure Analysis, 2015, 55: 224–245

[42]

Ma H, Lu Y, Wu Z, Vibration response analysis of a rotational shaft-disk-blade system with blade-tip rubbing. International Journal of Mechanical Sciences, 2016, 107: 110–125

[43]

Ma H, Pang X, Zeng J, Effects of gear crack propagation paths on vibration responses of the perforated gear system. Mechanical Systems and Signal Processing, 2015, 62–63: 113–128

[44]

Hu Z, Tang J, Zhong J, Frequency spectrum and vibration analysis of high speed gear-rotor system with tooth root crack considering transmission error excitation. Engineering Failure Analysis, 2016, 60: 405–441

[45]

Gui Y, Han Q, Chu F. A vibration model for fault diagnosis of planetary gearboxes with localized planet bearing defects. Journal of Mechanical Science and Technology, 2016, 30(9): 4109–4119

[46]

Tadina M, Boltežar M. Improved model of a ball bearing for the simulation of vibration signals due to faults during run-up. Journal of Sound and Vibration, 2011, 330(17): 4287–4301

[47]

Geng Z, Chen J, Barry Hull J. Analysis of engine vibration and design of an applicable diagnosing approach. International Journal of Mechanical Sciences, 2003, 45(8): 1391–1410

[48]

Wang H, Chen P. Fault diagnosis for a rolling bearing used in a reciprocating machine by adaptive filtering technique and fuzzy neural network. WSEAS Transactions on Systems, 2008, 7(1): 1–6

[49]

Wang H, Chen P. A feature extraction method based on information theory for fault diagnosis of reciprocating machinery. Sensors (Basel), 2009, 9(4): 2415–2436

[50]

Lee S, White P. The enhancement of impulsive noise and vibration signals for fault detection in rotating and reciprocating machinery. Journal of Sound and Vibration, 1998, 217(3): 485–505

[51]

Shen L, Tay F E, Qu L, Fault diagnosis using rough sets theory. Computers in Industry, 2000, 43(1): 61–72

[52]

Wang J, Hu H. Vibration-based fault diagnosis of pump using fuzzy technique. Measurement, 2006, 39(2): 176–185

[53]

El-Ghamry M, Reuben R, Steel J. The development of automated pattern recognition and statistical feature isolation techniques for the diagnosis of reciprocating machinery faults using acoustic emission. Mechanical Systems and Signal Processing, 2003, 17(4): 805–823

[54]

Östman F, Toivonen H T. Active torsional vibration control of reciprocating engines. Control Engineering Practice, 2008, 16(1): 78–88

[55]

Schultheis S M, Lickteig C A, Parchewsky R. Reciprocating compressor condition monitoring. In: Proceedings of 36th Turbomachinery Symposium. College Station, 2000, 10–13

[56]

Goodwin M, Nikolajsen J, Ogrodnik P. Reciprocating machinery bearing analysis: Theory and practice. Proceedings of the Institution of Mechanical Engineers. Part J: Journal of Engineering Tribology, 2003, 217(6): 409–426

[57]

Pituba J J C, Fernandes G R, de Souza Neto E A. Modeling of cohesive fracture and plasticity processes in composite microstructures. Journal of Engineering Mechanics, 2016, 142(10): 04016069

[58]

Zuo H, Yang Z, Chen X, Analysis of laminated composite plates using wavelet finite element method and higher-order plate theory. Composite Structures, 2015, 131: 248–258

[59]

Cavalcante M A, Khatam H, Pindera M J. Homogenization of elastic-plastic periodic materials by FVDAM and FEM approaches—An assessment. Composites Part B: Engineering, 2011, 42(6): 1713–1730

[60]

Pindera M J, Khatam H, Drago A S, Micromechanics of spatially uniform heterogeneous media: A critical review and emerging approaches. Composites Part B: Engineering, 2009, 40(5): 349–378

[61]

Cavalcante M A, Pindera M J, Khatam H. Finite-volume micromechanics of periodic materials: Past, present and future. Composites Part B: Engineering, 2012, 43(6): 2521–2543

[62]

Tu W, Pindera M J. Cohesive zone-based damage evolution in periodic materials via finite-volume homogenization. Journal of Applied Mechanics, 2014, 81(10): 101005

[63]

Chen Q, Chen X, Zhai Z, Micromechanical modeling of viscoplastic behavior of laminated polymer composites with thermal residual stress effect. Journal of Engineering Materials and Technology, 2016, 138(3): 031005

[64]

Chen Q, Chen X, Zhai Z, A new and general formulation of three-dimensional finite-volume micromechanics for particulate reinforced composites with viscoplastic phases. Composites Part B: Engineering, 2016, 85: 216–232

[65]

Joosse P, Blanch M, Dutton A, Acoustic emission monitoring of small wind turbine blades. Journal of Solar Energy Engineering, 2002, 124(4): 446–454

[66]

Schroeder K, Ecke W, Apitz J, A fibre Bragg grating sensor system monitors operational load in a wind turbine rotor blade. Measurement Science & Technology, 2006, 17(5): 1167–1172

[67]

Tian S, Yang Z, Chen X, Damage detection based on static strain responses using FBG in a wind turbine blade. Sensors (Basel), 2015, 15(8): 19992–20005

[68]

Mitra M, Gopalakrishnan S. Guided wave based structural health monitoring: A review. Smart Materials and Structures, 2016, 25(5): 053001

[69]

Raghavan A, Cesnik C E. Review of guided-wave structural health monitoring. Shock and Vibration Digest, 2007, 39(2): 91–114

[70]

Park H W, Kim S B, Sohn H. Understanding a time reversal process in Lamb wave propagation. Wave Motion, 2009, 46(7): 451–467

[71]

Park H W, Sohn H, Law K H, Time reversal active sensing for health monitoring of a composite plate. Journal of Sound and Vibration, 2007, 302(1–2): 50–66

[72]

Lin J, Gao F, Luo Z, High-resolution Lamb wave inspection in viscoelastic composite laminates. IEEE Transactions on Industrial Electronics, 2016, 63(11): 6989–6998

[73]

Lin J, Hua J, Zeng L, Excitation waveform design for lamb wave pulse compression. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2016, 63(1): 165–177

[74]

Hall J S, Michaels J E. Minimum variance ultrasonic imaging applied to an in situ sparse guided wave array. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2010, 57(10): 2311–2323

[75]

Hall J S, McKeon P, Satyanarayan L, Minimum variance guided wave imaging in a quasi-isotropic composite plate. Smart Materials and Structures, 2011, 20(2): 025013

[76]

Levine R M, Michaels J E. Model-based imaging of damage with Lamb waves via sparse reconstruction. Journal of the Acoustical Society of America, 2013, 133(3): 1525–1534

[77]

Li X, Yang Z, Zhang H, Crack growth sparse pursuit for wind turbine blade. Smart Materials and Structures, 2015, 24(1): 015002

[78]

Duan R, Wang F. Fault diagnosis of on-load tap-changer in converter transformer based on time-frequency vibration analysis. IEEE Transactions on Industrial Electronics, 2016, 63(6): 3815–3823

[79]

Feng Z, Ma H, Zuo M J. Vibration signal models for fault diagnosis of planet bearings. Journal of Sound and Vibration, 2016, 370: 372–393

[80]

Kumar H, Sugumaran V, Amarnath M. Fault diagnosis of bearings through sound signal using statistical features and bayes classifier. Journal of Vibration Engineering & Technologies, 2016, 4(2): 87–96

[81]

Lu S, Wang X, He Q, Fault diagnosis of motor bearing with speed fluctuation via angular resampling of transient sound signals. Journal of Sound and Vibration, 2016, 385: 16–32

[82]

Pahon E, Yousfi-Steiner N, Jemei S, A non-intrusive signal-based method for a proton exchange membrane fuel cell fault diagnosis. Fuel Cells, 2016, 17: 238–246

[83]

Pei P, Li Y, Xu H, A review on water fault diagnosis of PEMFC associated with the pressure drop. Applied Energy, 2016, 173: 366–385

[84]

Ohtsu M, Enoki M, Mizutani Y, Principles of the acoustic emission (AE) method and signal processing. In: The Japanese Society for Non-Destructive Inspection, ed. Practical Acoustic Emission Testing. Tokyo: Springer, 2016, 5–34

[85]

Cong F, Chen J, Dong G, Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis. Journal of Sound and Vibration, 2013, 332(8): 2081–2097

[86]

Yoon J, He D, Van Hecke B. On the use of a single piezoelectric strain sensor for wind turbine planetary gearbox fault diagnosis. IEEE Transactions on Industrial Electronics, 2015, 62(10): 6585–6593

[87]

Wei P, Dai Z, Zheng L, Fault diagnosis of the rolling bearing with optical fiber Bragg grating vibration sensor. Proceedings of SPIE: Optical Measurement Technology and Instrumentation, 2016, 101552I

[88]

Su Z, Ye L. Identification of damage using Lamb waves. London: Springer, 2009

[89]

Lynch J P, Loh K J. A summary review of wireless sensors and sensor networks for structural health monitoring. Shock and Vibration Digest, 2006, 38(2): 91–128

[90]

Huang J, Chen G, Shu L, WSNs-based mechanical equipment state monitoring and fault diagnosis in China. International Journal of Distributed Sensor Networks, 2015, 2015: 528464

[91]

Ricci R, Pennacchi P. Diagnostics of gear faults based on EMD and automatic selection of intrinsic mode functions. Mechanical Systems and Signal Processing, 2011, 25(3): 821–838

[92]

Mehrjou M R, Mariun N, Hamiruce Marhaban M, Rotor fault condition monitoring techniques for squirrel-cage induction machine—A review. Mechanical Systems and Signal Processing, 2011, 25(8): 2827–2848

[93]

Randall R B, Antoni J. Rolling element bearing diagnostics—A tutorial. Mechanical Systems and Signal Processing, 2011, 25(2): 485–520

[94]

Zhang Y, Randall R. Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram. Mechanical Systems and Signal Processing, 2009, 23(5): 1509–1517

[95]

Boškoski P, Petrovčič J, Musizza B, Detection of lubrication starved bearings in electrical motors by means of vibration analysis. Tribology International, 2010, 43(9): 1683–1692

[96]

Borghesani P, Pennacchi P, Chatterton S. The relationship between kurtosis-and envelope-based indexes for the diagnostic of rolling element bearings. Mechanical Systems and Signal Processing, 2014, 43(1–2): 25–43

[97]

Wang Y, Xiang J, Markert R, Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications. Mechanical Systems and Signal Processing, 2016, 66–67: 679–698

[98]

Barszcz T, Randall R B. Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine. Mechanical Systems and Signal Processing, 2009, 23(4): 1352–1365

[99]

Dwyer R. Detection of non-Gaussian signals by frequency domain kurtosis estimation. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing. Boston: IEEE, 1983, 607–610

[100]

Dwyer R. Use of the kurtosis statistic in the frequency domain as an aid in detecting random signals. IEEE Journal of Oceanic Engineering, 1984, 9(2): 85–92

[101]

Vrabie V, Granjon P, Serviere C. Spectral kurtosis: From definition to application. In: Proceedings of 6th IEEE International Workshop on Nonlinear Signal and Image Processing (NSIP 2003). Grado-Trieste: IEEE, 2003

[102]

Antoni J. The spectral kurtosis: A useful tool for characterising non-stationary signals. Mechanical Systems and Signal Processing, 2006, 20(2): 282–307

[103]

Antoni J, Randall R. The spectral kurtosis: Application to the vibratory surveillance and diagnostics of rotating machines. Mechanical Systems and Signal Processing, 2006, 20(2): 308–331

[104]

Antoni J. Fast computation of the kurtogram for the detection of transient faults. Mechanical Systems and Signal Processing, 2007, 21(1): 108–124

[105]

Randall R.Applications of spectral kurtosis in machine diagnostics and prognostics. Key Engineering Materials, 2005, 293 294: 21–32

[106]

Liu H, Huang W, Wang S, Adaptive spectral kurtosis filtering based on Morlet wavelet and its application for signal transients detection. Signal Process, 2014, 96, Part A: 118–124

[107]

Lei Y, Lin J, He Z, Application of an improved kurtogram method for fault diagnosis of rolling element bearings. Mechanical Systems and Signal Processing, 2011, 25(5): 1738–1749

[108]

Wang Y, Liang M. An adaptive SK technique and its application for fault detection of rolling element bearings. Mechanical Systems and Signal Processing, 2011, 25(5): 1750–1764

[109]

Barszcz T, JabŁoński A. A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram. Mechanical Systems and Signal Processing, 2011, 25(1): 431–451

[110]

Borghesani P, Pennacchi P, Randall R, Application of cepstrum pre-whitening for the diagnosis of bearing faults under variable speed conditions. Mechanical Systems and Signal Processing, 2013, 36(2): 370–384

[111]

Smith W A, Fan Z, Peng Z, Optimised spectral kurtosis for bearing diagnostics under electromagnetic interference. Mechani cal Systems and Signal Processing, 2016, 75: 371–394

[112]

Wang D, Tse P W, Tsui K L. An enhanced kurtogram method for fault diagnosis of rolling element bearings. Mechanical Systems and Signal Processing, 2013, 35(1–2): 176–199

[113]

Sawalhi N, Randall R, Endo H. The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis. Mechanical Systems and Signal Processing, 2007, 21(6): 2616–2633

[114]

He D, Wang X, Li S, Identification of multiple faults in rotating machinery based on minimum entropy deconvolution combined with spectral kurtosis. Mechanical Systems and Signal Processing, 2016, 81: 235–249

[115]

Luo J, Yu D, Liang M. A kurtosis-guided adaptive demodulation technique for bearing fault detection based on tunable-Q wavelet transform. Measurement Science & Technology, 2013, 24(5): 055009

[116]

Patel V, Tandon N, Pandey R. Defect detection in deep groove ball bearing in presence of external vibration using envelope analysis and Duffing oscillator. Measurement, 2012, 45(5): 960–970

[117]

Tian J, Morillo C, Azarian M H, Motor bearing fault detection using spectral kurtosis-based feature extraction coupled with K-nearest neighbor distance analysis. IEEE Transactions on Industrial Electronics, 2016, 63(3): 1793–1803

[118]

Immovilli F, Cocconcelli M, Bellini A, Detection of generalized-roughness bearing fault by spectral-kurtosis energy of vibration or current signals. IEEE Transactions on Industrial Electronics, 2009, 56(11): 4710–4717

[119]

Leite V C M N, Borges da Silva J G, Veloso G F C, Detection of localized bearing faults in induction machines by spectral kurtosis and envelope analysis of stator current. IEEE Transactions on Industrial Electronics, 2015, 62(3): 1855–1865

[120]

Fournier E, Picot A, Régnier J, Current-based detection of mechanical unbalance in an induction machine using spectral kurtosis with reference. IEEE Transactions on Industrial Electronics, 2015, 62(3): 1879–1887

[121]

Eftekharnejad B, Carrasco M, Charnley B, The application of spectral kurtosis on acoustic emission and vibrations from a defective bearing. Mechanical Systems and Signal Processing, 2011, 25(1): 266–284

[122]

Ruiz-Cárcel C, Hernani-Ros E, Cao Y, Use of spectral kurtosis for improving signal to noise ratio of acoustic emission signal from defective bearings. Journal of Failure Analysis and Prevention, 2014, 14(3): 363–371

[123]

Johnson J T, Potter L C. Performance study of algorithms for detecting pulsed sinusoidal interference in microwave radiometry. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(2): 628–636

[124]

Dion J L, Tawfiq I, Chevallier G. Harmonic component detection: Optimized Spectral Kurtosis for operational modal analysis. Mechanical Systems and Signal Processing, 2012, 26: 24–33

[125]

Lee J H, Seo J S. Application of spectral kurtosis to the detection of tip vortex cavitation noise in marine propeller. Mechanical Systems and Signal Processing, 2013, 40(1): 222–236

[126]

Nita G M, Gary D E. Statistics of the spectral kurtosis estimator. Publications of the Astronomical Society of the Pacific, 2010, 122(891): 595–607

[127]

Millioz F, Martin N. Circularity of the STFT and spectral kurtosis for time-frequency segmentation in Gaussian environment. IEEE Transactions on Signal Processing, 2011, 59(2): 515–524

[128]

Antoni J, Randall R. Differential diagnosis of gear and bearing faults. Journal of Vibration and Acoustics, 2002, 124(2): 165–171

[129]

Cui L, Wang J, Lee S. Matching pursuit of an adaptive impulse dictionary for bearing fault diagnosis. Journal of Sound and Vibration, 2014, 333(10): 2840–2862

[130]

He G, Ding K, Lin H. Fault feature extraction of rolling element bearings using sparse representation. Journal of Sound and Vibration, 2016, 366: 514–527

[131]

Tang H, Chen J, Dong G. Sparse representation based latent components analysis for machinery weak fault detection. Mechani cal Systems and Signal Processing, 2014, 46(2): 373–388

[132]

Zhang H, Chen X, Du Z, Nonlocal sparse model with adaptive structural clustering for feature extraction of aero-engine bearings. Journal of Sound and Vibration, 2016, 368: 223–248

[133]

Zhang H, Chen X, Du Z, Kurtosis based weighted sparse model with convex optimization technique for bearing fault diagnosis. Mechanical Systems and Signal Processing, 2016, 80: 349–376

[134]

He Q, Ding X. Sparse representation based on local time-frequency template matching for bearing transient fault feature extraction. Journal of Sound and Vibration, 2016, 370: 424–443

[135]

Wang S, Cai G, Zhu Z, Transient signal analysis based on Levenberg-Marquardt method for fault feature extraction of rotating machines. Mechanical Systems and Signal Processing, 2015, 54–55: 16–40

[136]

Wang S, Huang W, Zhu Z. Transient modeling and parameter identification based on wavelet and correlation filtering for rotating machine fault diagnosis. Mechanical Systems and Signal Processing, 2011, 25(4): 1299–1320

[137]

Fan W, Cai G, Zhu Z, Sparse representation of transients in wavelet basis and its application in gearbox fault feature extraction. Mechanical Systems and Signal Processing, 2015, 56–57: 230–245

[138]

Qiao B, Zhang X, Gao J, Sparse deconvolution for the large-scale ill-posed inverse problem of impact force reconstruction. Mechanical Systems and Signal Processing, 2016, 83: 93–115

[139]

Qiao B, Zhang X, Wang C, Sparse regularization for force identification using dictionaries. Journal of Sound and Vibration, 2016, 368: 71–86

[140]

Lin J, Hu Z, Chen Z, Sparse reconstruction of blade tip-timing signals for multi-mode blade vibration monitoring. Mechanical Systems and Signal Processing, 2016, 81: 250–258

[141]

Ding Y, He W, Chen B, Detection of faults in rotating machinery using periodic time-frequency sparsity. Journal of Sound and Vibration, 2016, 382: 357–378

[142]

He W, Ding Y, Zi Y, Sparsity-based algorithm for detecting faults in rotating machines. Mechanical Systems and Signal Processing, 2016, 72–73: 46–64

[143]

He W, Ding Y, Zi Y, Repetitive transients extraction algorithm for detecting bearing faults. Mechanical Systems and Signal Processing, 2017, 84, Part A: 227–244

[144]

Donoho D L. Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4): 1289–1306

[145]

Tang G, Yang Q, Wang H, Sparse classification of rotating machinery faults based on compressive sensing strategy. Mechatronics, 2015, 31: 60–67

[146]

Chen X, Du Z, Li J, Compressed sensing based on dictionary learning for extracting impulse components. Signal Process, 2014, 96, Part A: 94–109

[147]

Du Z, Chen X, Zhang H, Sparse feature identification based on union of redundant dictionary for wind turbine gearbox fault diagnosis. IEEE Transactions on Industrial Electronics, 2015, 62(10): 6594–6605

[148]

Chen X, Cai G, Cao H, Condition assessment for automatic tool changer based on sparsity-enabled signal decomposition method. Mechatronics, 2015, 31: 50–59

[149]

Wang Y, Xiang J, Mo Q, Compressed sparse time-frequency feature representation via compressive sensing and its applications in fault diagnosis. Measurement, 2015, 68: 70–81

[150]

Qiao B, Zhang X, Gao J, Impact-force sparse reconstruction from highly incomplete and inaccurate measurements. Journal of Sound and Vibration, 2016, 376: 72–94

[151]

Feng Z, Liang M, Chu F. Recent advances in time-frequency analysis methods for machinery fault diagnosis: A review with application examples. Mechanical Systems and Signal Processing, 2013, 38(1): 165–205

[152]

Peng Z, Chu F. Application of the wavelet transform in machine condition monitoring and fault diagnostics: A review with bibliography. Mechanical Systems and Signal Processing, 2004, 18(2): 199–221

[153]

Yan R, Gao R X, Chen X. Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing, 2014, 96: 1–15

[154]

Auger F, Flandrin P, Lin Y T, Time-frequency reassignment and synchrosqueezing: An overview. IEEE Signal Processing Magazine, 2013, 30(6): 32–41

[155]

Daubechies I, Lu J, Wu H T. Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 2011, 30(2): 243–261

[156]

Li C, Liang M. Time-frequency signal analysis for gearbox fault diagnosis using a generalized synchrosqueezing transform. Mechanical Systems and Signal Processing, 2012, 26: 205–217

[157]

Li C, Liang M. A generalized synchrosqueezing transform for enhancing signal time-frequency representation. Signal Processing, 2012, 92(9): 2264–2274

[158]

Feng Z, Chen X, Liang M. Iterative generalized synchrosqueezing transform for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions. Mechanical Systems and Signal Processing, 2015, 52–53: 360–375

[159]

Shi J, Liang M, Necsulescu D S, Generalized stepwise demodulation transform and synchrosqueezing for time-frequency analysis and bearing fault diagnosis. Journal of Sound and Vibration, 2016, 368: 202–222

[160]

Cao H, Xi S, Chen X, Zoom synchrosqueezing transform and iterative demodulation: Methods with application. Mechanical Systems and Signal Processing, 2016, 72–73: 695–711

[161]

Wang S, Chen X, Li G, Matching demodulation transform with application to feature extraction of rotor rub-impact fault. IEEE Transactions on Instrumentation and Measurement, 2014, 63(5): 1372–1383

[162]

Sheu Y L, Hsu L Y, Wu H T. Analysis of signals with fast-varying instantaneous frequency: Window selection and insights from synchrosqueezing transform. arXiv preprint arXiv:1512.04811, 2015

[163]

Wang S, Chen X, Tong C, Matching synchrosqueezing wavelet transform and application to aeroengine vibration monitoring. IEEE Transactions on Instrumentation and Measurement, 2017, 66(2): 360–372

[164]

Wang S, Chen X, Wang Y, Nonlinear squeezing time-frequency transform for weak signal detection. Signal Processing, 2015, 113: 195–210

[165]

Wang S, Yang L, Chen X, Nonlinear squeezing time-frequency transform and application in rotor rub-impact fault diagnosis. Journal of Manufacturing Science and Engineering, 2017 (in press)

[166]

Peng Z, Meng G, Chu F, Polynomial chirplet transform with application to instantaneous frequency estimation. IEEE Transactions on Instrumentation and Measurement, 2011, 60(9): 3222–3229

[167]

Yang Y, Zhang W, Peng Z, Multicomponent signal analysis based on polynomial chirplet transform. IEEE Transactions on Industrial Electronics, 2013, 60(9): 3948–3956

[168]

Yang Y, Peng Z, Meng G, Spline-kernelled Chirplet transform for the analysis of signals with time-varying frequency and its application. IEEE Transactions on Industrial Electronics, 2012, 59(3): 1612–1621

[169]

Yang Y, Peng Z, Meng G, Characterize highly oscillating frequency modulation using generalized Warblet transform. Mechanical Systems and Signal Processing, 2012, 26: 128–140

[170]

Yang Y, Peng Z, Dong X, General parameterized time-frequency transform. IEEE Transactions on Signal Processing, 2014, 62(11): 2751–2764

[171]

Yang Y, Peng Z, Dong X, Application of parameterized time-frequency analysis on multicomponent frequency modulated signals. IEEE Transactions on Instrumentation and Measurement, 2014, 63(12): 3169–3180

[172]

Yang W, Tavner P J, Tian W. Wind turbine condition monitoring based on an improved spline-kernelled chirplet transform. IEEE Transactions on Industrial Electronics, 2015, 62(10): 6565–6574

[173]

Yang Y, Peng Z, Zhang W, Dispersion analysis for broadband guided wave using generalized Warblet transform. Journal of Sound and Vibration, 2016, 367: 22–36

[174]

Yang Y, Peng Z, Dong X, Nonlinear time-varying vibration system identification using parametric time-frequency transform with spline kernel. Nonlinear Dynamics, 2016, 85(3): 1679–1693

[175]

Wang S, Chen X, Cai G, Matching demodulation transform and synchrosqueezing in time-frequency analysis. IEEE Transactions on Signal Processing, 2014, 62(1): 69–84

[176]

Bouzida A, Touhami O, Ibtiouen R, Fault diagnosis in industrial induction machines through discrete wavelet transform. IEEE Transactions on Industrial Electronics, 2011, 58(9): 4385–4395

[177]

Bin G, Gao J, Li X, Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network. Mechanical Systems and Signal Processing, 2012, 27: 696–711

[178]

Seshadrinath J, Singh B, Panigrahi B K. Investigation of vibration signatures for multiple fault diagnosis in variable frequency drives using complex wavelets. IEEE Transactions on Power Electronics, 2014, 29(2): 936–945

[179]

Bayram I, Selesnick I W. Overcomplete discrete wavelet transforms with rational dilation factors. IEEE Transactions on Signal Processing, 2009, 57(1): 131–145

[180]

Bayram I, Selesnick I W. Frequency-domain design of overcomplete rational-dilation wavelet transforms. IEEE Transactions on Signal Processing, 2009, 57(8): 2957–2972

[181]

Selesnick I W. Wavelet transform with tunable Q-factor. IEEE Transactions on Signal Processing, 2011, 59(8): 3560–3575

[182]

Chen B, Zhang Z, Sun C, Fault feature extraction of gearbox by using overcomplete rational dilation discrete wavelet transform on signals measured from vibration sensors. Mechanical Systems and Signal Processing, 2012, 33: 275–298

[183]

He W, Zi Y, Chen B, Tunable Q-factor wavelet transform denoising with neighboring coefficients and its application to rotating machinery fault diagnosis. Science China. Technological Sciences, 2013, 56(8): 1956–1965

[184]

Wang H, Chen J, Dong G. Feature extraction of rolling bearing’s early weak fault based on EEMD and tunable Q-factor wavelet transform. Mechanical Systems and Signal Processing, 2014, 48(1–2): 103–119

[185]

Wang X, Zi Y, He Z. Multiwavelet denoising with improved neighboring coefficients for application on rolling bearing fault diagnosis. Mechanical Systems and Signal Processing, 2011, 25(1): 285–304

[186]

Yuan J, He Z, Zi Y, Construction and selection of lifting-based multiwavelets for mechanical fault detection. Mechanical Systems and Signal Processing, 2013, 40(2): 571–588

[187]

Jiang H, Li C, Li H. An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis. Mechanical Systems and Signal Processing, 2013, 36(2): 225–239

[188]

Sun H, He Z, Zi Y, Multiwavelet transform and its applications in mechanical fault diagnosis—A review. Mechanical Systems and Signal Processing, 2014, 43(1–2): 1–24

[189]

Lei Y, Lin J, He Z, A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mechani cal Systems and Signal Processing, 2013, 35(1–2): 108–126

[190]

Babu T R, Srikanth S, Sekhar A S. Hilbert-Huang transform for detection and monitoring of crack in a transient rotor. Mechanical Systems and Signal Processing, 2008, 22(4): 905–914

[191]

Lin L, Chu F. HHT-based AE characteristics of natural fatigue cracks in rotating shafts. Mechanical Systems and Signal Processing, 2012, 26: 181–189

[192]

Guo D, Peng Z. Vibration analysis of a cracked rotor using Hilbert-Huang transform. Mechanical Systems and Signal Processing, 2007, 21(8): 3030–3041

[193]

Zhang K, Yan X. Multi-cracks identification method for cantilever beam structure with variable cross-sections based on measured natural frequency changes. Journal of Sound and Vibration, 2017, 387: 53–65

[194]

Chandra N H, Sekhar A. Fault detection in rotor bearing systems using time frequency techniques. Mechanical Systems and Signal Processing, 2016, 72–73: 105–133

[195]

Xu L. Study on fault detection of rolling element bearing based on translation-invariant denoising and Hilbert-Huang transform. Journal of Computers, 2012, 7(5): 1142–1146

[196]

Li Q, Wang H. A research review of Hilbert-Huang transform used for rolling bearing fault diagnosis. Applied Mechanics and Materials, 2013, 397400: 2152–2155

[197]

Lei Y, He Z, Zi Y. Application of the EEMD method to rotor fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 2009, 23(4): 1327–1338

[198]

Feng Z, Zuo M J, Hao R, Ensemble empirical mode decomposition-based Teager energy spectrum for bearing fault diagnosis. Journal of Vibration and Acoustics, 2013, 135(3): 031013

[199]

Wu T Y, Chen J, Wang C. Characterization of gear faults in variable rotating speed using Hilbert-Huang transform and instantaneous dimensionless frequency normalization. Mechanical Systems and Signal Processing, 2012, 30: 103–122

[200]

Smith J S. The local mean decomposition and its application to EEG perception data. Journal of the Royal Society, Interface, 2005, 2(5): 443–454

[201]

Wang Y, He Z, Zi Y. A demodulation method based on improved local mean decomposition and its application in rub-impact fault diagnosis. Measurement Science and Technology, 2009, 20(2): 025704

[202]

Cheng J, Yang Y, Yang Y. A rotating machinery fault diagnosis method based on local mean decomposition. Digital Signal Processing, 2012, 22(2): 356–366

[203]

Feng Z, Zuo M J, Qu J, Joint amplitude and frequency demodulation analysis based on local mean decomposition for fault diagnosis of planetary gearboxes. Mechanical Systems and Signal Processing, 2013, 40(1): 56–75

[204]

Liu H, Han M. A fault diagnosis method based on local mean decomposition and multi-scale entropy for roller bearings. Mechanism and Machine Theory, 2014, 75: 67–78

[205]

Wang Y, Markert R, Xiang J, Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system. Mechanical Systems and Signal Processing, 2015, 60–61: 243–251

[206]

Widodo A, Yang B S. Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing, 2007, 21(6): 2560–2574

[207]

Jardine A K S, Lin D, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 2006, 20(7): 1483–1510

[208]

Jia F, Lei Y, Lin J, Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems and Signal Processing, 2016, 72–73: 303–315

[209]

Roemer M J, Hong C, Hesler S H. Machine health monitoring and life management using finite-element-based neural networks. Journal of Engineering for Gas Turbines and Power, 1996, 118(4): 830–835

[210]

Li B, Chow M Y, Tipsuwan Y, Neural-network-based motor rolling bearing fault diagnosis. IEEE Transactions on Industrial Electronics, 2000, 47(5): 1060–1069

[211]

Fan Y, Li C J. Diagnostic rule extraction from trained feedforward neural networks. Mechanical Systems and Signal Processing, 2002, 16(6): 1073–1081

[212]

Gebraeel N Z, Lawley M A. A neural network degradation model for computing and updating residual life distributions. IEEE Transactions on Automation Science and Engineering, 2008, 5(1): 154–163

[213]

Vyas N S, Satishkumar D. Artificial neural network design for fault identification in a rotor-bearing system. Mechanism and Machine Theory, 2001, 36(2): 157–175

[214]

Jack L, Nandi A. Comparison of neural networks and support vector machines in condition monitoring applications. In: Proceedings of 13th International Congress on Condition Monitoring and Diagnostic Engineering Management. 2000, 721–730

[215]

Saravanan N, Siddabattuni V K, Ramachandran K. Fault diagnosis of spur bevel gear box using artificial neural network (ANN), and proximal support vector machine (PSVM). Applied Soft Computing, 2010, 10(1): 344–360

[216]

Nguyen N T, Lee H H, Kwon J M. Optimal feature selection using genetic algorithm for mechanical fault detection of induction motor. Journal of Mechanical Science and Technology, 2008, 22(3): 490–496

[217]

Spoerre J. Application of the cascade correlation algorithm (CCA) to bearing fault classification problems. Computers in Industry, 1997, 32(3): 295–304

[218]

Baillie D, Mathew J. A comparison of autoregressive modeling techniques for fault diagnosis of rolling element bearings. Mechanical Systems and Signal Processing, 1996, 10(1): 1–17

[219]

Li C J, Huang T Y. Automatic structure and parameter training methods for modeling of mechanical systems by recurrent neural networks. Applied Mathematical Modelling, 1999, 23(12): 933–944

[220]

Deuszkiewicz P, Radkowski S. On-line condition monitoring of a power transmission unit of a rail vehicle. Mechanical Systems and Signal Processing, 2003, 17(6): 1321–1334

[221]

Nyanteh Y D.Application of artificial intelligence to rotating machine condition monitoring. Dissertation for the Doctoral Degree. Tallahassee: The Florida State University, 2013

[222]

Samanta B. Artificial neural networks and genetic algorithms for gear fault detection. Mechanical Systems and Signal Processing, 2004, 18(5): 1273–1282

[223]

Wang C C, Too G P J. Rotating machine fault detection based on HOS and artificial neural networks. Journal of Intelligent Manufacturing, 2002, 13(4): 283–293

[224]

Wang H, Gao J, Jiang Z, Rotating machinery fault diagnosis based on EEMD time-frequency energy and SOM neural network. Arabian Journal for Science and Engineering, 2014, 39(6): 5207–5217

[225]

Yang D, Liu Y, Li S, Gear fault diagnosis based on support vector machine optimized by artificial bee colony algorithm. Mechanism and Machine Theory, 2015, 90: 219–229

[226]

Widodo A, Kim E Y, Son J D, Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine. Expert Systems with Applications, 2009, 36(3): 7252–7261

[227]

Guo L, Chen J, Li X. Rolling bearing fault classification based on envelope spectrum and support vector machine. Journal of Vibration and Control, 2009, 15(9): 1349–1363

[228]

Liu Z, Cao H, Chen X, Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings. Neurocomputing, 2013, 99: 399–410

[229]

Samanta B, Nataraj C. Use of particle swarm optimization for machinery fault detection. Engineering Applications of Artificial Intelligence, 2009, 22(2): 308–316

[230]

Seera M, Lim C P, Nahavandi S, Condition monitoring of induction motors: A review and an application of an ensemble of hybrid intelligent models. Expert Systems with Applications, 2014, 41(10): 4891–4903

[231]

Shen C, Wang D, Kong F, Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier. Measurement, 2013, 46(4): 1551–1564

[232]

Tabrizi A, Garibaldi L, Fasana A, Early damage detection of roller bearings using wavelet packet decomposition, ensemble empirical mode decomposition and support vector machine. Meccanica, 2015, 50(3): 865–874

[233]

Rajeswari C, Sathiyabhama B, Devendiran S, Diagnostics of gear faults using ensemble empirical mode decomposition, hybrid binary bat algorithm and machine learning algorithms. Journal of Vibroengineering, 2015, 17(3): 1169–1187

[234]

Tran V T, AlThobiani F, Ball A. An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks. Expert Systems with Applications, 2014, 41(9): 4113–4122

[235]

Ma M, Chen X, Wang S, Bearing degradation assessment based on weibull distribution and deep belief network. In: Proceedings of International Symposium on Flexible Automation (ISFA). Cleveland: IEEE, 382–385

[236]

Tao J, Liu Y, Yang D. Bearing fault diagnosis based on deep belief network and multisensor information fusion. Shock and Vibration, 2016, 2016: 1–9

[237]

Chen Z, Li C, Sánchez R V. Multi-layer neural network with deep belief network for gearbox fault diagnosis. Journal of Vibroengineering, 2015, 17(5): 2379–2392

[238]

Shao S, Sun W, Wang P, Learning features from vibration signals for induction motor fault diagnosis. In: Proceedings of International Symposium on Flexible Automation (ISFA). Cleveland: IEEE, 2016, 71–76

[239]

Tamilselvan P, Wang P. Failure diagnosis using deep belief learning based health state classification. Reliability Engineering & System Safety, 2013, 115: 124–135

[240]

Gan M, Wang C. Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings. Mechanical Systems and Signal Processing, 2016, 7273: 92–104

[241]

Li C, Sánchez R V, Zurita G, Fault diagnosis for rotating machinery using vibration measurement deep statistical feature learning. Sensors (Basel), 2016, 16(6): 895

[242]

Guo X, Shen C, Chen L. Deep fault recognizer: An integrated model to denoise and extract features for fault diagnosis in rotating machinery. Applied Sciences, 2016, 7(1): 41

[243]

Ahmed H, Wong M D, Nandi A. Effects of deep neural network parameters on classification of bearing faults. In: Proceedings of 42nd Annual Conference of the IEEE on Industrial Electronics Society. IEEE, 2016, 6329–6334

[244]

Xin G, Antoni J, Hamzaoui N.An exploring study of hidden Markov model in rolling element bearing diagnostis. In: Proceedings of Surveillance 8. Roanne, 2015

[245]

Bunks C, McCarthy D, Al-Ani T. Condition-based maintenance of machines using hidden Markov models. Mechanical Systems and Signal Processing, 2000, 14(4): 597–612

[246]

Dong M, He D. Hidden semi-Markov models for machinery health diagnosis and prognosis transactions of NAMRI. Transactions of the North American Manufacturing Research Institute of SME, 2004, 32: 199–206

[247]

Xu Y, Ge M. Hidden Markov model-based process monitoring system. Journal of Intelligent Manufacturing, 2004, 15(3): 337–350

[248]

Ye D, Ding Q, Wu Z. New method for faults diagnosis of rotating machinery based on 2-dimension hidden Markov model. In: Proceedings of the International Symposium on Precision Mechanical Measurement. 2002, 391–395

[249]

Zhou H, Chen J, Dong G, Detection and diagnosis of bearing faults using shift-invariant dictionary learning and hidden Markov model. Mechanical Systems and Signal Processing, 2016, 72–73: 65–79

[250]

Baydar N, Chen Q, Ball A, Detection of incipient tooth defect in helical gears using multivariate statistics. Mechanical Systems and Signal Processing, 2001, 15(2): 303–321

[251]

Gómez González A, Fassois S D. A supervised vibration-based statistical methodology for damage detection under varying environmental conditions & its laboratory assessment with a scale wind turbine blade. Journal of Sound and Vibration, 2016, 366: 484–500

[252]

Mao Z, Todd M. A model for quantifying uncertainty in the estimation of noise-contaminated measurements of transmissibi lity. Mechanical Systems and Signal Processing, 2012, 28: 470–481

[253]

Song L, Chen P, Wang H, Intelligent condition diagnosis method for rotating machinery based on probability density and discriminant analyses. IEEE Signal Processing Letters, 2016, 23(8): 1111–1115

[254]

Lei Y, He Z, Zi Y. A new approach to intelligent fault diagnosis of rotating machinery. Expert Systems with Applications, 2008, 35(4): 1593–1600

[255]

Wang C C, Kang Y, Liao C C. Using Bayesian networks in gear fault diagnosis. Applied Mechanics and Materials, 2013, 284287: 2416–2420

[256]

Mao Z, Todd M D. A Bayesian recursive framework for ball-bearing damage classification in rotating machinery. Structural Health Monitoring, 2016, 15(6): 668–684

[257]

Wang D, Sun S, Tse P W. A general sequential Monte Carlo method based optimal wavelet filter: A Bayesian approach for extracting bearing fault features. Mechanical Systems and Signal Processing, 2015, 52–53: 293–308

[258]

Wang D, Tsui K L, Zhou Q. Novel Gauss-Hermite integration based Bayesian inference on optimal wavelet parameters for bearing fault diagnosis. Mechanical Systems and Signal Processing, 2016, 72–73: 80–91

[259]

Qu L, Liu X, Peyronne G, The holospectrum: A new method for rotor surveillance and diagnosis. Mechanical Systems and Signal Processing, 1989, 3(3): 255–267

[260]

Qu L, Qiu H, Xu G. Rotor balancing based on holospectrum analysis principle and practice. China Mechanical Engineering, 1998, 9(1): 60–63 (in Chinese)

[261]

Liu S, Qu L. A new field balancing method of rotor systems based on holospectrum and genetic algorithm. Applied Soft Computing, 2008, 8(1): 446–455

[262]

Li B, Chen X, Ma J, Detection of crack location and size in structures using wavelet finite element methods. Journal of Sound and Vibration, 2005, 285(4–5): 767–782

[263]

Xiang J, Chen X, Li B, Identification of a crack in a beam based on the finite element method of a B-spline wavelet on the interval. Journal of Sound and Vibration, 2006, 296(4–5): 1046–1052

[264]

Xiang J, Chen X, Mo Q, Identification of crack in a rotor system based on wavelet finite element method. Finite Elements in Analysis and Design, 2007, 43(14): 1068–1081

[265]

Chen X, Yang S, Ma J, The construction of wavelet finite element and its application. Finite Elements in Analysis and Design, 2004, 40(5–6): 541–554

[266]

Gao J, Ma B, Jiang Z. Research on fault self-recovery engineering. Journal of Dalian University of Technology, 2006, 46(3): 460–468 (in Chinese)

[267]

Gao J.Thinking about future plant medicine engineering. Engineering Science, 2003, 5(12): 30–35 (in Chinese)

[268]

Gao J.Research on the fault self-recovery principle of equipment system. Engineering Science, 2005, 7(5): 43–48 (in Chinese)

[269]

Wen B. Recent development of vibration utilization engineering. Frontiers of Mechanical Engineering in China, 2008, 3(1): 1–9

[270]

Wen B, Li Y, Zhang Y, Vibration Utilization Engineering. Beijing: Science Press, 2005 (in Chinese)

[271]

Wen B, Zhao C, Su D, Vibration Synchronization and Controlled Synchronization. Beijing: Science Press, 2003 (in Chinese)

[272]

Wen B, Liu S, He Q. Theory and Dynamic Design Methods of Vibrating Machinery. Beijing: Mechanical Industry Press, 2001 (in Chinese)

[273]

Wen B, Liu F. Theory of Vibrating Machines and Its Applications. Beijing: Machine Press, 1982 (in Chinese)

[274]

NSFC. 2017. Retrieved from

[275]

Center for Intelligent Maintenance Systems. 2017. Retrieved from

RIGHTS & PERMISSIONS

The Author(s) 2018. This article is published with open access at link.springer.com and journal.hep.com.cn

AI Summary AI Mindmap
PDF (875KB)

7163

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/