Manifold Learning Using Linear Local Tangent Space Alignment (LLTSA) Algorithm for Noise Removal in Wavelet Filtered Vibration Signal

Anil Kumar1, Rajesh Kumar1
1Precision Metrology Laboratory, Department of Mechanical Engineering, Sant Longowal Institute of Engineering and Technology, Longowal 148 106, India

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Dolenc, B., Boškoski, P., Đani, J.: Distributed bearing fault diagnosis based on vibration analysis. Mech. Syst. Signal Process. 66–67, 521–532 (2016)

Randall, R.B., Antoni, J.: Rolling element bearing diagnostics—a tutorial. Mech. Syst. Signal Process. 24, 485–520 (2011)

Sheen, Y.-T.: An analysis method for the vibration signal with amplitude modulation in a bearing system. J. Sound Vib. 303, 538–552 (2007)

McFadden, P.D., Smith, J.D.: Vibration monitoring of rolling element bearings by the high-frequency resonance technique—a review. Tribol. Int. 17, 3–10 (1984)

Antoni, J.: The spectral kurtosis: a useful tool for characterising non-stationary signals. Mech. Syst. Signal Process. 20, 282–307 (2006)

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

Antoni, J., Randall, R.B.: The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines. Mech. Syst. Signal Process. 20, 308–331 (2006)

Hussain, S., Hossam, G.A.: Gearbox fault detection using real coded genetic algorithm and novel shock response spectrum features extraction. J. Nondestruct. Eval. 33, 111–123 (2014)

Xiang, J., Zhong, Y., Gao, H.: Rolling element bearing fault detection using PPCA and spectral kurtosis. Measurement 75, 180–191 (2015)

Bingzhen, J., Jiawei, X., Yanxue, W.: Rolling bearing fault diagnosis approach based on PPCA denoising and cyclic bispectrum method. J. Vib. Control 22, 2420–2433 (2016)

Meng, L., Jiawei, X., Zhong, Y., Song, W.: Fault diagnosis of rolling bearing based on second generation wavelet denoising and morphological filter. J. Mech. Sci. Technol. 29, 3121–3129 (2015)

Bin, G.F., Gao, J.J., Li, X.J., Dhillon, B.S.: Early fault diagnosis of rotating machinery based on wavelet packets–Empirical mode decomposition feature extraction and neural network. Mech. Syst. Signal Process. 27, 696–711 (2012)

Lei, Y., He, Z., Zi, Y.: EEMD method and WNN for fault diagnosis of locomotive roller bearings. Expert Syst. Appl. 38, 7334–7341 (2011)

Meng, L., Xiang, J., Wang, Y., Jiang, Y., Gao, H.: A hybrid fault diagnosis method using morphological filter-translation invariant wavelet and improved ensemble empirical mode decomposition. Mech. Syst. Signal Process. 50–51, 101–115 (2015)

EL-Morsy, M.S., Abouel-seoud, S., Rabeih, E.A.: Gearbox Damage Diagnosis using Wavelet Transform Technique. Int. J. Acoust. Vib. 16, 173–179 (2011)

Shao, R., Hu, W., Wang, Y., Qi, X.: The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform. Measurement 54, 118–132 (2014)

Ozturk, H., Yesilyurt, I., Sabuncu, M.: Detection and advancement monitoring of distributed pitting failure in gears. J. Nondestruct. Eval. 29, 63–73 (2010)

Kumar, R., Kumar, A.: Fusion of microphone and accelerometer sensing for the identification and measurement of inner race defect. In: Measurement 2015, Proceedings of the 10 $$^{\rm th}$$ th International Conference, Smolenice, Slovakia, pp. 183–186 (2015)

Talhaoui, H., Menacer, A., Kessal, A., Kechida, R.: Fast Fourier and discrete wavelet transforms applied to sensorless vector control induction motor for rotor bar faults diagnosis. ISA Trans. 53, 1639–1649 (2014)

Kumar, R., Singh, M.: Outer race defect width measurement in taper roller bearing using discrete wavelet transform of vibration signal. Measurement 46, 537–545 (2013)

Kankar, P.K., Sharma, S.C., Harsha, S.P.: Rolling element bearing fault diagnosis using autocorrelation and continuous wavelet transform. J. Vib. Control. 17, 2081–2094 (2011)

Bordoloi, D.J., Tiwari, R.: Support vector machine based optimization of multi-fault classification of gears with evolutionary algorithms from time-frequency vibration data. Measurement. 55, 1–14 (2014)

Wang, Y., Xu, G., Liang, L., Jiang, K.: Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis. Mech. Syst. Signal Process. 54–55, 259–276 (2015)

Wang, J., He, Q., Kong, F.: Multiscale envelope manifold for enhanced fault diagnosis of rotating machines. Mech. Syst. Signal Process. 52–53, 376–392 (2015)

Maaten, L. J. P., Postma, E. O., Herik, H. J.: Dimensionality reduction: a comparative review. Tilburg University Technical Report. TiCC-TR 2009-005. http://www.tilburguniversity.edu/upload/59afb3b8-21a5-4c78-8eb3-6510597382db_TR2009005.pdf (2009). Accessed 21 Nov 2015

Maaten, L. J. P.: An Introduction to Dimensionality Reduction Using Matlab. Universiteit Maastricht. The Netherlands, Report MICC 07-07. http://www.arabic-icr.googlecode.com/svn/trunk/Code/External20Lib/drtoolbox/Paper20on20DR.pdf (2007). Accessed 21 Nov 2015

Zhang, T., Yang, J., Zhao, D., Ge, X.: Linear local tangent space alignment and application to face recognition. Neurocomputing 70, 1547–1553 (2007)

Mallat, S.: A Wavelet Tour of Signal Processing. Academic Press, San Diego (1999)

He, W., Zi, Y., Chen, B., Feng, Wu, He, Z.: Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform. Mech. Syst. Signal Process. 54–55, 457–480 (2015)