Omni-Directional Regeneration (ODR) of Gap Sensor Signal for Journal Bearing System Diagnosis



Joon Ha Jung Byung Chul Jeon Byeng D. Youn Donghwan Kim Yeonwhan Kim


We have developed a technique that enhances the detectability of sensors used to acquire data from a journal bearing rotor system. Usually, at an axial position for the rotating shaft on a journal bearing system, two sensors are fixed in radial direction at a right angle. The conventional diagnosis researches use only the acquired signals. However, two fixed sensors may not give sufficient information for diagnosis of the system since anomalies can happen in arbitrary direction. To improve the robustness of the diagnosis, coordinate transformed gap sensor signal is generated in arbitrary direction without installing extra sensors or adjusting sensor positions. With the original signals, the generated signals are used in the process of diagnosis. The powerful but simple method is described in the paper, and is verified by data sets from the experiment.

How to Cite

Ha Jung, J. ., Chul Jeon, B. ., D. Youn, B. ., Kim, D. ., & Kim, Y. . (2015). Omni-Directional Regeneration (ODR) of Gap Sensor Signal for Journal Bearing System Diagnosis. Annual Conference of the PHM Society, 7(1).
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signal processing, data-driven phm, Journal Bearing

Bachschmid, N., Pennacchi, P., & Vania, A. (2004). Diagnostic significance of orbit shape analysis and its application to improve machine fault detection. Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 26, pp. 200-208.

Bo, F., Jian-Zhong, Z., Wen-Qing, C., & Bing-Hui, Y. (2004). Identification of the shaft orbits for turbine rotor by modified Fourier descriptors. Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on, 26-29 Aug. 2004.

Bonnardot, F., El Badaoui, M., Randall, R., Daniere, J., & Guillet, F. (2005). Use of the acceleration signal of a gearbox in order to perform angular resampling (with limited speed fluctuation). Mechanical Systems and Signal Processing, vol. 19(4), pp. 766-785.

Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol., vol. 2(3), pp. 1-27.

Chen, C.-S., & Chen, J.-S. (2011). Rotor fault diagnosis system based on sGA-based individual neural networks. Expert Systems with Applications, vol. 38(9), pp. 10822- 10830.

Fengqi, W., & Meng, G. (2006). Compound rub malfunctions feature extraction based on full-spectrum cascade analysis and SVM. Mechanical Systems and Signal Processing, vol. 20(8), pp. 2007-2021. DOI: 10.1016/j.ymssp.2005.10.004

Goldman, P., & Muszynska, A. (1999). Application of full spectrum to rotating machinery diagnostics. Orbit, vol. 20(1), pp. 17-21.

Guo, B., Damper, R. I., Gunn, S. R., & Nelson, J. D. B. (2008). A fast separability-based feature-selection method for high-dimensional remotely sensed image classification. Pattern Recognition, vol. 41(5), pp. 1653- 1662.

Jeon, B., Jung, J., Youn, B. D., Kim, Y., & Bae, Y. C. (2014). Statistical Approach to Diagnostic Rules for Various Malfunctions of Journal Bearing System Using Fisher Discriminant Analysis. Second European Conference of the PHM Society 2014, July 8-10, Nantes, France.

Liu, B. (2003). Adaptive harmonic wavelet transform with applications in vibration analysis. Journal of Sound and Vibration, vol. 262(1), pp. 45-64. DOI:

Malhi, A., & Gao, R. X. (2004). PCA-based feature selection scheme for machine defect classification. IEEE Transactions on Instrumentation and Measurement, vol.53(6), pp. 1517-1525. DOI: 10.1109/TIM.2004.834070

Patel, T., & Darpe, A. (2009). Use of full spectrum cascade for rotor rub identification. Adv. Vib. Eng, vol. 8, pp.139-151.

Patel, T. H., & Darpe, A. K. (2009). Experimental investigations on vibration response of misaligned rotors. Mechanical Systems and Signal Processing, vol. 23(7), pp. 2236-2252.

Patel, T. H., & Darpe, A. K. (2011). Application of full spectrum analysis for rotor fault diagnosis. IUTAM Symposium on Emerging Trends in Rotor Dynamics.

Sanz, J., Perera, R., & Huerta, C. (2007). Fault diagnosis of rotating machinery based on auto-associative neural networks and wavelet transforms. Journal of Sound and Vibration, vol. 302(4–5), pp. 981-999. DOI:

Sun, W., Chen, J., & Li, J. (2007). Decision tree and PCA- based fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, vol. 21(3), pp. 1300- 1317. DOI:

Villa, L. F., Reñones, A., Perán, J. R., & De Miguel, L. J. (2011). Angular resampling for vibration analysis in wind turbines under non-linear speed fluctuation. Mechanical Systems and Signal Processing, vol. 25(6), pp. 2157-2168.

Wang, C., Zhou, J., Kou, P., Luo, Z., & Zhang, Y. (2012). Identification of shaft orbit for hydraulic generator unit using chain code and probability neural network. Applied Soft Computing, vol. 12(1), pp. 423-429. DOI: 10.1016/j.asoc.2011.08.028

Wang, H., Wang, H., & Ji, Y. (2013). Orbit identification method based on ISOMAP for rotor system fault diagnosis. 2013 IEEE 11th International Conference on Electronic Measurement & Instruments (ICEMI), 16-19 Aug. 2013.

Yan, C., Zhang, H., Li, H., Li, Y., & Huang, W. (2009). Automatic Identification of Shaft Orbits for Steam Turbine Generator Sets. WRI Global Congress on Intelligent Systems, 2009. GCIS '09. , 19-21 May 2009.

Yan, C., Zhang, H., & Wu, L. (2010). Automatic Recognition of Orbit Shape for Fault Diagnosis in Steam Turbine Generator Sets. Journal of Computational Information Systems, vol. 6(6), pp. 1995-2008.

Zhao, X., Patel, T. H., & Zuo, M. J. (2012). Multivariate EMD and full spectrum based condition monitoring for rotating machinery. Mechanical Systems and Signal Processing, vol. 27(0), pp. 712-728. DOI:
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