Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery

In the light of the significance of the rotating machinery and the possible severe losses resulted from its unexpected defects, it is vital and meaningful to exploit the effective and feasible diagnostic methods of its faults. Among them, the emphasis of the analysis approaches for fault type and severity is on the extraction of useful components in the fault features. On account of the common cyclostationarity of vibration signal under faulty states, fault diagnosis methods based on cyclostationary analysis play an essential role in the rotatory machine. Based on it, the fundamental definition and classification of cyclostationarity are introduced briefly. The mathematical principles of the essential cyclic spectral analysis are outlined. The significant applications of cyclostationary theory are highlighted in the fault diagnosis of the main rotating machinery, involving bearing, gear, and pump. Finally, the widely-used methods on the basis of cyclostationary theory are concluded, and the potential research directions are prospected.

[1]  K. Gryllias,et al.  Cyclostationary-based Multiband Envelope Spectra Extraction for bearing diagnostics: The Combined Improved Envelope Spectrum , 2021 .

[2]  Jimeng Li,et al.  An enhanced rolling bearing fault detection method combining sparse code shrinkage denoising with fast spectral correlation. , 2020, ISA transactions.

[3]  Miao He,et al.  A new hybrid deep signal processing approach for bearing fault diagnosis using vibration signals , 2020, Neurocomputing.

[4]  Yangyang Wang,et al.  Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects , 2019, Chinese Journal of Mechanical Engineering.

[5]  Ioannis Antoniadis,et al.  CYCLOSTATIONARY ANALYSIS OF ROLLING-ELEMENT BEARING VIBRATION SIGNALS , 2001 .

[6]  Paolo Pennacchi,et al.  Testing second order cyclostationarity in the squared envelope spectrum of non-white vibration signals , 2013 .

[7]  Chuan Wang,et al.  Multi-Disciplinary Optimization Design of Axial-Flow Pump Impellers Based on the Approximation Model , 2020, Energies.

[8]  Fulei Chu,et al.  Spectrum auto-correlation analysis and its application to fault diagnosis of rolling element bearings , 2013 .

[9]  Marco Buzzoni,et al.  A tool for validating and benchmarking signal processing techniques applied to machine diagnosis , 2020 .

[10]  Zhiliang Liu,et al.  ACCUGRAM: A novel approach based on classification to frequency band selection for rotating machinery fault diagnosis. , 2019, ISA transactions.

[11]  Shouqi Yuan,et al.  Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery , 2020, IEEE Access.

[12]  Anand Parey,et al.  Extraction of weak fault transients using variational mode decomposition for fault diagnosis of gearbox under varying speed , 2020 .

[13]  Yong Zhu,et al.  Deep Learning-Based Intelligent Fault Diagnosis Methods Toward Rotating Machinery , 2020, IEEE Access.

[14]  Shouqi Yuan,et al.  Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery , 2020, IEEE Access.

[15]  Qiang Miao,et al.  Weighted Cyclic Harmonic-to-Noise Ratio for Rolling Element Bearing Fault Diagnosis , 2020, IEEE Transactions on Instrumentation and Measurement.

[16]  Yaguo Lei,et al.  Envelope harmonic-to-noise ratio for periodic impulses detection and its application to bearing diagnosis , 2016 .

[17]  Min Li,et al.  Fault feature extraction of rolling bearing based on an improved cyclical spectrum density method , 2015 .

[18]  Sanjay H Upadhyay,et al.  A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings , 2016 .

[19]  Jérôme Antoni,et al.  Order-frequency analysis of machine signals , 2017 .

[20]  Robert B. Randall,et al.  THE RELATIONSHIP BETWEEN SPECTRAL CORRELATION AND ENVELOPE ANALYSIS IN THE DIAGNOSTICS OF BEARING FAULTS AND OTHER CYCLOSTATIONARY MACHINE SIGNALS , 2001 .

[21]  Chuan Wang,et al.  Influence of Critical Wall Roughness on the Performance of Double-Channel Sewage Pump , 2020 .

[22]  Pietro Borghesani,et al.  Optimal demodulation-band selection for envelope-based diagnostics: A comparative study of traditional and novel tools , 2019 .

[23]  Federico Campanini,et al.  A Methodology Based on Cyclostationary Analysis for Fault Detection of Hydraulic Axial Piston Pumps , 2018, Energies.

[24]  Robert B. Randall,et al.  Simulating gear and bearing interactions in the presence of faults. Part I. The combined gear bearing dynamic model and the simulation of localised bearing faults , 2008 .

[25]  Konstantinos Gryllias,et al.  Vibration-Based Condition Monitoring of Wind Turbine Gearboxes Based on Cyclostationary Analysis , 2018, Journal of Engineering for Gas Turbines and Power.

[26]  Robert B. Randall,et al.  Extraction of second-order cyclostationary sources—Application to vibration analysis , 2005 .

[27]  Robert B. Randall,et al.  On the use of the cyclic power spectrum in rolling element bearings diagnostics , 2005 .

[28]  Yaguo Lei,et al.  Condition monitoring and fault diagnosis of planetary gearboxes: A review , 2014 .

[29]  H.A. Toliyat,et al.  Condition Monitoring and Fault Diagnosis of Electrical Motors—A Review , 2005, IEEE Transactions on Energy Conversion.

[30]  Guolin He,et al.  Non-stationary vibration feature extraction method based on sparse decomposition and order tracking for gearbox fault diagnosis , 2018 .

[31]  Gaoyong Luo,et al.  Real-Time Condition Monitoring by Significant and Natural Frequencies Analysis of Vibration Signal with Wavelet Filter and Autocorrelation Enhancement , 2000 .

[32]  P. Borghesani,et al.  A faster algorithm for the calculation of the fast spectral correlation , 2018, Mechanical Systems and Signal Processing.

[33]  Andrew Ball,et al.  Autocorrelated Envelopes for early fault detection of rolling bearings , 2021, Mechanical Systems and Signal Processing.

[34]  Shouqi Yuan,et al.  Cyclic Spectral Analysis of Vibration Signals for Centrifugal Pump Fault Characterization , 2018, IEEE Sensors Journal.

[35]  H. Vincent Poor,et al.  Fault Diagnostics Using Statistical Change Detection in the Bispectral Domain , 2000 .

[36]  Konstantinos Gryllias,et al.  Cyclostationary modeling for local fault diagnosis of planetary gear vibration signals , 2020 .

[37]  Antonio Napolitano,et al.  Cyclostationarity: Half a century of research , 2006, Signal Process..

[38]  Shouqi Yuan,et al.  Energy loss evaluation in a side channel pump under different wrapping angles using entropy production method , 2020 .

[39]  Emiliano Mucchi,et al.  An algorithm for the simulation of faulted bearings in non-stationary conditions , 2018 .

[40]  Gang Tang,et al.  Underdetermined blind separation of bearing faults in hyperplane space with variational mode decomposition , 2019, Mechanical Systems and Signal Processing.

[41]  Antonio Napolitano,et al.  Cyclostationarity: Limits and generalizations , 2016, Signal Process..

[42]  W. Bennett Statistics of regenerative digital transmission , 1958 .

[43]  Xinyu Li,et al.  A semi-supervised convolutional neural network-based method for steel surface defect recognition , 2020, Robotics Comput. Integr. Manuf..

[44]  Yaguo Lei,et al.  Applications of machine learning to machine fault diagnosis: A review and roadmap , 2020 .

[45]  Qiang Miao,et al.  Multiple Discolored Cyclic Harmonic Ratio Diagram Based on Meyer Wavelet Filters for Rotating Machine Fault Diagnosis , 2020, IEEE Sensors Journal.

[46]  Jin Chen,et al.  Noise resistant time frequency analysis and application in fault diagnosis of rolling element bearings , 2012 .

[47]  V. Purushotham,et al.  Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition , 2005 .

[48]  Dong Wang,et al.  Some further thoughts about spectral kurtosis, spectral L2/L1 norm, spectral smoothness index and spectral Gini index for characterizing repetitive transients , 2018 .

[49]  Cécile Capdessus,et al.  CYCLOSTATIONARY PROCESSES: APPLICATION IN GEAR FAULTS EARLY DIAGNOSIS , 2000 .

[50]  Jérôme Antoni,et al.  Indicators of cyclostationarity: Theory and application to gear fault monitoring , 2008 .

[51]  Robert B. Randall,et al.  Use of mesh phasing to locate faulty planet gears , 2019, Mechanical Systems and Signal Processing.

[52]  Robert B. Randall,et al.  Bearing diagnostics under strong electromagnetic interference based on Integrated Spectral Coherence , 2020, Mechanical Systems and Signal Processing.

[53]  Radoslaw Zimroz,et al.  Cyclic sources extraction from complex multiple-component vibration signal via periodically time varying filter , 2017 .

[54]  Antonio Napolitano,et al.  Cyclostationarity: New trends and applications , 2016, Signal Process..

[55]  Christian Steinebach,et al.  Intelligent diagnosis and maintenance management , 1998, J. Intell. Manuf..

[56]  Jérôme Antoni,et al.  Cyclostationary modelling of rotating machine vibration signals , 2004 .

[57]  Nacer Hamzaoui,et al.  Extraction of second-order cyclostationary sources by matching instantaneous power spectrum with stochastic model – application to wind turbine gearbox , 2020 .

[58]  Bongtae Han,et al.  Autocorrelation-based time synchronous averaging for condition monitoring of planetary gearboxes in wind turbines , 2016 .

[59]  Aouni A. Lakis,et al.  Application of Cyclic Spectral Analysis in Diagnosis of Bearing Faults in Complex Machinery , 2015 .

[60]  Jérôme Antoni,et al.  Time-frequency approach to extraction of selected second-order cyclostationary vibration components for varying operational conditions , 2013 .

[61]  G. R. Sabareesh,et al.  Comparison of condition monitoring techniques in assessing fault severity for a wind turbine gearbox under non-stationary loading , 2019, Mechanical Systems and Signal Processing.

[62]  Yosra Marnissi,et al.  Optimal filtering of angle-time cyclostationary signals: Application to vibrations recorded under nonstationary regimes , 2020 .

[63]  Robert B. Randall,et al.  A Stochastic Model for Simulation and Diagnostics of Rolling Element Bearings With Localized Faults , 2003 .

[64]  Lei Zhang,et al.  Data driven nonlinear dynamical systems identification using multi-step CLDNN , 2019, AIP Advances.

[65]  Asoke K. Nandi,et al.  CYCLOSTATIONARITY IN ROTATING MACHINE VIBRATIONS , 1998 .

[66]  Shouqi Yuan,et al.  Effect of URANS and Hybrid RANS-Large Eddy Simulation Turbulence Models on Unsteady Turbulent Flows Inside a Side Channel Pump , 2020 .

[67]  Fengshou Gu,et al.  An Improved Cyclic Modulation Spectral Analysis Based on the CWT and Its Application on Broken Rotor Bar Fault Diagnosis for Induction Motors , 2019, Applied Sciences.

[68]  Anand Parey,et al.  Gearbox fault diagnosis under non-stationary conditions with independent angular re-sampling technique applied to vibration and sound emission signals , 2017, Applied Acoustics.

[69]  Wenyi Liu,et al.  Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution , 2010 .

[70]  Jérôme Antoni,et al.  The spectral analysis of cyclo-non-stationary signals , 2016 .

[71]  Tomasz Barszcz,et al.  A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram , 2011 .

[72]  Wanlu Jiang,et al.  Bifurcation Characteristic Research on the Load Vertical Vibration of a Hydraulic Automatic Gauge Control System , 2019, Processes.

[73]  Tarek Kebabsa,et al.  Application of the cyclostationarity analysis in the detection of mechanical defects: comparative study , 2019, The International Journal of Advanced Manufacturing Technology.

[74]  W. Cioch,et al.  Finding a frequency signature for a cyclostationary signal with applications to wheel bearing diagnostics , 2013 .

[75]  Sanjay H Upadhyay,et al.  Bearing performance degradation assessment based on a combination of empirical mode decomposition and k-medoids clustering , 2017 .

[76]  W. Gardner The spectral correlation theory of cyclostationary time-series , 1986 .

[77]  J. Antoni Fast computation of the kurtogram for the detection of transient faults , 2007 .

[78]  Huaxia Chen,et al.  A Low-Complexity Detection Method for Statistical Signals in OFDM Systems , 2014, IEEE Communications Letters.

[79]  Wanlu Jiang,et al.  Amplitude-frequency characteristics analysis for vertical vibration of hydraulic AGC system under nonlinear action , 2019, AIP Advances.

[80]  E. Powers,et al.  Digital Bispectral Analysis and Its Applications to Nonlinear Wave Interactions , 1979, IEEE Transactions on Plasma Science.

[81]  J. Antoni,et al.  Fast computation of the spectral correlation , 2017 .

[82]  François Guillet,et al.  Blind separation of convolved cyclostationary processes , 2005, Signal Process..

[83]  Yong Zhu,et al.  Extraction method for signal effective component based on extreme-point symmetric mode decomposition and Kullback–Leibler divergence , 2019, Journal of the Brazilian Society of Mechanical Sciences and Engineering.

[84]  Jérôme Antoni,et al.  Angle⧹time cyclostationarity for the analysis of rolling element bearing vibrations , 2015 .

[85]  Yao Cheng,et al.  Blind deconvolution assisted with periodicity detection techniques and its application to bearing fault feature enhancement , 2020 .

[86]  Yong Qin,et al.  A simple and fast guideline for generating enhanced/squared envelope spectra from spectral coherence for bearing fault diagnosis , 2019, Mechanical Systems and Signal Processing.

[87]  Konstantinos Gryllias,et al.  A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks , 2020 .

[88]  Pietro Borghesani,et al.  The envelope-based cyclic periodogram , 2015 .

[89]  Konstantinos Gryllias,et al.  A pre-processing methodology to enhance novel information for rotating machine diagnostics , 2019, Mechanical Systems and Signal Processing.

[90]  Guangming Dong,et al.  Application of the horizontal slice of cyclic bispectrum in rolling element bearings diagnosis , 2012 .

[91]  Marco Buzzoni,et al.  Blind deconvolution based on cyclostationarity maximization and its application to fault identification , 2018, Journal of Sound and Vibration.

[92]  Jun Terada,et al.  TDD-Based Rapid Fault Detection and Recovery for Fronthaul Bridged Network , 2018, IEEE Communications Letters.

[93]  A. F. Stronach,et al.  Third-order spectral techniques for the diagnosis of motor bearing condition using artificial neural networks , 2002 .

[94]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[95]  Giorgio Dalpiaz,et al.  Effectiveness and Sensitivity of Vibration Processing Techniques for Local Fault Detection in Gears , 2000 .

[96]  Radoslaw Zimroz,et al.  On-line updating of cyclostationary tools for fault detection in rotating machines - the filter bank approach , 2017 .

[97]  Yi Wang,et al.  M-band flexible wavelet transform and its application to the fault diagnosis of planetary gear transmission systems , 2019 .