Diagnostics 101: A Tutorial for Fault Diagnostics of Rolling Element Bearing Using Envelope Analysis in MATLAB

[1]  Ioannis Antoniadis,et al.  Rolling element bearing fault diagnosis using wavelet packets , 2002 .

[2]  Jay Lee,et al.  Methodology and Framework for Predicting Helicopter Rolling Element Bearing Failure , 2012, IEEE Transactions on Reliability.

[3]  Zhaoheng Liu,et al.  A new approach based on OMA-empirical wavelet transforms for bearing fault diagnosis , 2016 .

[4]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[5]  Byeng D. Youn,et al.  A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: shallow and deep learning , 2019, JMST Advances.

[6]  Qiang Miao,et al.  Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators , 2018, IEEE Access.

[7]  Joo-Ho Choi,et al.  Remaining useful life prediction of rolling element bearings using degradation feature based on amplitude decrease at specific frequencies , 2018 .

[8]  Joo-Ho Choi,et al.  Convolutional neural network for gear fault diagnosis based on signal segmentation approach , 2018, Structural Health Monitoring.

[9]  S. A. McInerny,et al.  Basic vibration signal processing for bearing fault detection , 2003, IEEE Trans. Educ..

[10]  Nadège Bouchonneau,et al.  A review of wind turbine bearing condition monitoring: State of the art and challenges , 2016 .

[11]  Chaeyoung Lim,et al.  Feature extraction for bearing prognostics using weighted correlation of fault frequencies over cycles , 2020 .

[12]  Peter J. Kootsookos,et al.  MODELING OF LOW SHAFT SPEED BEARING FAULTS FOR CONDITION MONITORING , 1998 .

[13]  Eric Bechhoefer,et al.  Bearing envelope analysis window selection Using spectral kurtosis techniques , 2011, 2011 IEEE Conference on Prognostics and Health Management.

[14]  Robert B. Randall,et al.  The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis , 2007 .

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

[16]  Wahyu Caesarendra,et al.  A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing , 2017 .

[17]  Fred L. Ramsey,et al.  Characterization of the Partial Autocorrelation Function , 1974 .

[18]  Robert B. Randall,et al.  Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study , 2015 .

[19]  Xinghui Zhang.  Bearing Run-To-Failure Data Simulation for Condition Based Maintenance , 2014 .

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

[21]  Diego Cabrera,et al.  A review on data-driven fault severity assessment in rolling bearings , 2018 .

[22]  S. Vrieze.  Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). , 2012, Psychological methods.

[23]  Jay Lee,et al.  Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .