Novelty detection in airport baggage conveyor gear-motors using Synchro-squeezing transform and Self-organizing maps



Budhaditya Hazra Shilpa Pantula Sriram Narasimhan


A powerful continuous wavelet transform based signal processing tool named Synchro-squeezing transform (SST) has recently emerged in the context of non-stationary signal processing. Founded upon the premise of time-frequency (TF) reassignment, its basic objective is to provide a sharper representation of signals in the TF plane. Additionally, it can also extract the individual components of a non- stationary multi-component signal, which makes it attractive for rotating machinery signals. This work utilizes the decomposing power of SST transform to extract useful components from gear-motor signals in relevant sub-bands, followed by the application of standard rotating machinery condition indicators. For timely detection of faults in airport baggage conveyor gear-motors, a novelty detection technique based on the recently developed concepts of self-organizing maps (SOM) is applied on the condition indicators. This approach promises improved anomaly detection pow

er than that can be achieved by applying condition indicators and SOM directly to the inherently complex raw-data. Data collected from the airport baggage conveyor gear-motors provides the test bed to demonstrate the efficacy of the proposed approach.

How to Cite

Hazra, B. ., Pantula, S. ., & Narasimhan, S. . (2013). Novelty detection in airport baggage conveyor gear-motors using Synchro-squeezing transform and Self-organizing maps. Annual Conference of the PHM Society, 5(1).
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rotating machinery

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

Cohen, L. (1995). Time-Frequency Analysis. Prentice-Hall, Englewood Cliffs. NJ, USA.

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

Hazra, B., and Narasimhan, S. (2013), Rotating machinery diagnosis using synchro-squeezing transform based feature analysis. Proceedings of MFPT Conference. May 13-17, Cleveland, Ohio.

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

Kohonen. T. (1990), The Self-Organizing Map. Proceedings of the IEEE, 8(9), pp. 1464–1480.

Lee, H. J., & Cho, S. (2005). SOM-based novelty detection using novel data. Intelligent Data Engineering and Automated Learning-IDEAL. Springer, Berlin Heidelberg, pp. 359-366.

Lei, Y., Lin, J., He, Z., and Zuo, M, J., (2013). A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 35(1-2), pp. 108-126.

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

Oberlin, T., Meignen, S., and Perrier, V. (2012), On the Mode Synthesis in the Synchrosqueezing method. Proceedings of EUSIPCO, Bucharest, Aug. 27-31.

Staszewski, W., Worden, K., and Tomlinson, G. (1997). Time frequency analysis in gearbox fault detection using the Wigner-Ville distribution and pattern recognition. Mechanical Systems and Signal Processing, 11(5), pp. 673–692.

Timusk, M., Lipsett, M., and Mechefske, C, K., (2008). Fault detection using transient machine signals. Mechanical Systems and Signal Processing. 22(7), pp. 1724-1749.

Vecer, P., kreidl, M., and Smid, R. (2005). Condition indicators for gearbox condition monitoring systems. Acta Polytechnica, 45(6), pp. 35-43.

Wang, W. and McFadden, P. (1995). Application of orthogonal wavelets to early gear damage detection. Mechanical Systems and Signal Processing, 9(5), 497– 507.

Worden, K., Manson, G., & Fieller, N. R. J. (2000). Damage detection using outlier analysis.Journal of Sound and Vibration, 229(3), 647-667.

Ypma, A., Leshem, A., and Duin, R. (2002). Blind separation of rotating machine sources: bilinear forms and convolutive mixtures. Neurocomputing, 49(1-4), 349-68.

Zhan, Y. and Jardine, A. (2005). Adaptive autoregressive modeling of non-stationary vibration signals under distinct gear states Part 1: modeling. Journal of Sound and Vibration, 286(6), 429-450
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