An Enhanced Cyclostationary Method and Its Application on the Incipient Fault Diagnosis of Induction Motors

[1]  Liangwei Zhang,et al.  A nearly end-to-end deep learning approach to fault diagnosis of wind turbine gearboxes under nonstationary conditions , 2023, Eng. Appl. Artif. Intell..

[2]  Xuefeng Chen,et al.  A novel denoising strategy based on sparse modeling for rotating machinery fault detection under time-varying operating conditions , 2023, Measurement.

[3]  Gang Yu,et al.  Adaptive synchroextracting transform and its application in bearing fault diagnosis. , 2023, ISA transactions.

[4]  Tongle Xu,et al.  Intelligent fault diagnosis of gearbox based on differential continuous wavelet transform-parallel multi-block fusion residual network , 2022, Measurement.

[5]  Zhiqin Zhu,et al.  A review of the application of deep learning in intelligent fault diagnosis of rotating machinery , 2022, Measurement.

[6]  Xian-gang Cao,et al.  Health indicator construction and status assessment of rotating machinery by spatio-temporal fusion of multi-domain mixed features , 2022, Measurement.

[7]  Yi Qin,et al.  Rotating machine fault diagnosis by a novel fast sparsity-enabled feature-energy-ratio method. , 2022, ISA transactions.

[8]  Tian Han,et al.  Compound faults diagnosis method for wind turbine mainshaft bearing with Teager and second-order stochastic resonance , 2022, Measurement.

[9]  A. Rahideh,et al.  Broken Rotor Bar and Rotor Eccentricity Fault Detection in Induction Motors Using a Combination of Discrete Wavelet Transform and Teager–Kaiser Energy Operator , 2022, IEEE Transactions on Energy Conversion.

[10]  Dongying Han,et al.  Stochastic resonance in a high-dimensional space coupled bistable system and its application , 2022, Applied Mathematical Modelling.

[11]  Zhile Wang,et al.  Positive role of bifurcation on stochastic resonance and its application in fault diagnosis under time-varying rotational speed , 2022, Journal of Sound and Vibration.

[12]  Lingli Cui,et al.  Flexible iterative generalized demodulation filtering for the fault diagnosis of rotating machinery under nonstationary conditions , 2022, Structural Health Monitoring.

[13]  Hao Wang,et al.  Power spectral density-guided variational mode decomposition for the compound fault diagnosis of rolling bearings , 2022, Measurement.

[14]  Xuezhi Zhao,et al.  Feature frequency extraction algorithm based on the singular value decomposition with changed matrix size and its application in fault diagnosis , 2022, Journal of Sound and Vibration.

[15]  Konstantinos Gryllias,et al.  Application of the Combined Teager-Kaiser Envelope for bearing fault diagnosis , 2021 .

[16]  Xiaoyang Zheng,et al.  Intelligent bearing fault diagnosis based on Teager energy operator demodulation and multiscale compressed sensing deep autoencoder , 2021, Measurement.

[17]  Prashant Tiwari,et al.  Novel self-adaptive vibration signal analysis: Concealed component decomposition and its application in bearing fault diagnosis , 2021, Journal of Sound and Vibration.

[18]  Arunandan Kumar,et al.  A fault frequency bands location method based on improved fast spectral correlation to extract fault features in axial piston pump bearings , 2020 .

[19]  Juan Manuel Munoz-Guijosa,et al.  Early fault detection of single-point rub in gas turbines with accelerometers on the casing based on continuous wavelet transform , 2020 .

[20]  Xiaoqin Zhou,et al.  Adaptive variational mode decomposition and its application to multi-fault detection using mechanical vibration signals. , 2020, ISA transactions.

[21]  Tian Tian,et al.  Compound Fault Diagnosis of Rolling Bearing Based on Singular Negentropy Difference Spectrum and Integrated Fast Spectral Correlation , 2020, Entropy.

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

[23]  Robert B. Randall,et al.  Uses and mis-uses of energy operators for machine diagnostics , 2019, Mechanical Systems and Signal Processing.

[24]  Fengshou Gu,et al.  Fault Identification of Broken Rotor Bars in Induction Motors Using an Improved Cyclic Modulation Spectral Analysis , 2019, Energies.

[25]  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.

[26]  Umut Fırat,et al.  Compressive Sensing for Detecting Ships With Second-Order Cyclostationary Signatures , 2018, IEEE Journal of Oceanic Engineering.

[27]  Huibin Lin,et al.  Sliding window denoising K-Singular Value Decomposition and its application on rolling bearing impact fault diagnosis , 2018 .

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

[29]  Ming J. Zuo,et al.  Atomic decomposition and sparse representation for complex signal analysis in machinery fault diagnosis: A review with examples , 2017 .

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

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

[32]  J. Antoni,et al.  Detection of Surface Ships From Interception of Cyclostationary Signature With the Cyclic Modulation Coherence , 2012, IEEE Journal of Oceanic Engineering.

[33]  J. Antoni Cyclostationarity by examples , 2009 .

[34]  J. Antoni Cyclic spectral analysis of rolling-element bearing signals : Facts and fictions , 2007 .

[35]  J. Antoni Cyclic spectral analysis in practice , 2007 .

[36]  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 .

[37]  J. F. Kaiser,et al.  On a simple algorithm to calculate the 'energy' of a signal , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[38]  B. Tang,et al.  Fault diagnosis of rotating machinery based on graph weighted reinforcement networks under small samples and strong noise , 2023, Mechanical Systems and Signal Processing.

[39]  X. Z. Cui,et al.  Simulating nonstationary non-Gaussian vector process based on continuous wavelet transform , 2022 .

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

[41]  Marc Thomas,et al.  A Frequency-Weighted Energy Operator and complementary ensemble empirical mode decomposition for bearing fault detection , 2017 .

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

[43]  Y. Shao,et al.  Variable-scale evolutionary adaptive mode denoising in the application of gearbox early fault diagnosis , 2022, Mechanical Systems and Signal Processing.