Smart Diagnosis of Journal Bearing Rotor Systems: Unsupervised Feature Extraction Scheme by Deep Learning

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Hyunseok Oh Byung Chul Jeon Joon Ha Jung Byeng D. Youn

Abstract

In most approaches for journal bearing rotor system diagnosis, dominant features are manually extracted based on expert’s experience and domain knowledge. With the adoption of advanced journal bearings and the limited knowledge about physics-of-failure, the current practice for feature extraction showed limitations for real applications in the power plant industry. To this end, this paper proposes an unsupervised scheme to extract features from correlated vibration signals. First, raw vibration signals from a pair of sensors are preprocessed by generating two-dimensional images. Second, the vibration images are characterized with a HOG (histogram of original gradients) descriptor. Then, deep learning is used to extract relevant features for journal bearing rotor system diagnosis. To demonstrate the validity of the proposed unsupervised-feature-extraction scheme, a case study was conducted with data from the RK4 rotor kit. The results showed that the proposed scheme outperformed existing methods in terms of fault classification accuracy. We anticipate that the proposed scheme is promising as it can minimize the reliance of expert’s experience and domain knowledge.

How to Cite

Oh, H., Jeon, B. C., Jung, J. H., & Youn, B. D. (2016). Smart Diagnosis of Journal Bearing Rotor Systems: Unsupervised Feature Extraction Scheme by Deep Learning. Annual Conference of the PHM Society, 8(1). https://doi.org/10.36001/phmconf.2016.v8i1.2533
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Keywords

deep learning, deep belief networks, unsupervised, diagnosis, journal bearings

References
Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, pp. 1798-1828. doi:10.1109/tpami.2013.50
Gan, M., Wang, C., & Zhu, C. (2016). Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings. Mechanical Systems and Signal Processing, vol. 72-73, pp. 92-104. doi:10.1016/j.ymssp.2015.11.014
Ha, J.M., Youn, B.D., Oh, H., Han, B., Jung, Y., & Park, J. (2016). Autocorrelation-based time synchronous averaging for condition monitoring of planetary gearboxes in wind turbines. Mechanical Systems and Signal Processing, vol. 70-71, pp. 161-175. doi:10.1016/j.ymssp.2015.09.040
Hinton, G.E. (1999). Produts of experts. In Proceedings of the Ninth International Conference on Artificial Neural Networks, September 7-10, Edinburgh, UK.
Hinton, G.E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, vol. 18, pp. 1527-1554. doi:10.1162/neco.2006.18.7.1527
Jeon, B.C., Jung, J.H., Youn, B.D., Kim, Y.-W., & Bae, Y.-C. (2015). Datum unit optimization for robustness of a journal bearing diagnosis system. International Journal of Precision Engineering and Manufacturing, vol. 16, 2411-2425. doi:10.1007/s12541-015-0311-y
Jia, F., Lei, Y., Lin, J., Zhou, X., & Lu, N. (2016). Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems and Signal Processing, vol. 72-73, pp. 303-315. doi:10.1016/j.ymssp.2015.10.025
Jung, J.H., Jeon, B.C., Youn, B.D., Kim, D., & Kim, Y. (2015) Omni-directional regeneration (ODR) of gap sensor signal for journal bearing system diagnosis, In Proceedings of the 7th Annual Conference of the Prognostics and Health Management Society, October 18-22. Coronado, CA
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning Nature, vol. 521, pp. 436-444. doi:10.1038/nature14539
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical Systems and Signal Processing, vol. 42, pp. 314-334. doi:10.1016/j.ymssp.2013.06.004
Oh, H., Han, B., McCluskey, P., Han, C., & Youn, B.D. (2015). Physics-of-failure, condition monitoring, and prognostics of insulated gate bipolar transistor modules: A review. IEEE Transactions on Power Electronics, vol. 30, pp. 2413-2426. doi:10.1109/tpel.2014.2346485
Sarkar, S., Lore, K., Sarkar, S., Ramanan, V., Chakravarthy, S., Phoha, S., & Ray, A. (2015). Early detection of combustion instability from hi-speed flame images via deep learning and symbolic time series analysis, In Proceedings of the 7th Annual Conference of the Prognostics and Health Management Society, October 18-22. Coronado, CA
Shao, H., Jiang, H., Zhang, X., & Niu, M. (2015). Rolling bearing fault diagnosis using an optimization deep belief network. Measurement Science and Technology, vol. 26, 115002. doi:10.1088/0957-0233/26/11/115002
Smolensky, P. (1986). Information processing in dynamical systems: foundations of harmony theory vol 1. Parallel distributed processing: explorations in the microstructure of cognition. MA, USA: MIT Press
Tamilselvan, P., & Wang, P. (2013). Failure diagnosis using deep belief learning based health state classification Reliability Engineering & System Safety, vol. 115, pp. 124-135. doi:10.1016/j.ress.2013.02.022
Yan, W., & Yu, L. (2015). On accurate and reliable anomaly detection for gas turbine combustors: a deep learning approach, In Proceedings of the 7th Annual Conference of the Prognostics and Health Management Society, October 18-22. Coronado, CA
Yang, B., & Kim, K.J. (2006). Application of Dempster-Shafer theory in fault diagnosis of induction motors using vibration and current signals. Mechanical Systems and Signal Processing, vol. 20, pp. 403-420.
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Technical Papers

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