Diagnosis of Tidal Turbine Vibration Data through Deep Neural Networks



Grant S. Galloway Victoria M. Catterson Thomas Fay Andrew Robb Craig Love


Tidal power is an emerging field of renewable energy, harnessing the power of regular and predictable tidal currents. However, maintenance of submerged equipment is a major challenge. Routine visual inspections of equipment must be performed onshore, requiring the costly removal of turbines from the sea bed and resulting in long periods of downtime. The development of condition monitoring techniques providing automated fault detection can therefore be extremely beneficial to this industry, reducing the dependency on inspections and allowing maintenance to be planned efficiently.
This paper investigates the use of deep learning approaches for fault detection within a tidal turbine’s generator from vibration data. Training and testing data were recorded over two deployment periods of operation from an accelerometer sensor placed within the nacelle of the turbine, representing ideal and faulty responses over a range of operating conditions. The paper evaluates a deep learning approach through a stacked autoencoder network in comparison to feature-based classification methods, where features have been extracted over varying rotation speeds through the Vold-Kalman filter.

How to Cite

Galloway, G. S., Catterson, V. M., Fay, T., Robb, A., & Love, C. (2016). Diagnosis of Tidal Turbine Vibration Data through Deep Neural Networks. PHM Society European Conference, 3(1). https://doi.org/10.36001/phme.2016.v3i1.1603
Abstract 63 | PDF Downloads 47



Deep Learning, Vibration, Tidal Energy

Altman, N. S. (1992). An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression. The American Statistician, 46(3), pp. 175-185. doi:10.1080/00031305.1992.10475879
Catterson, V. M., & Sheng, B. (2015). Deep Neural Networks for Understanding and Diagnosing Partial Discharge Data. IEEE Electrical Insulation Conference (pp. 218-221). June 7-10, Seattle, WA. doi: 10.1109/ICACACT.2014.7223616
Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, pp. 273-297. doi: 10.1023/A:1022627411411
Henríquez, P., Alonso, J. B., Ferrer, M. A., & Travieso, C. M. (2014). Review of Automatic Fault Diagnosis Systems Using Audio and Vibration Signals. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(5), pp. 642-652. doi: 10.1109/TSMCC.2013.2257752
Hinton, G. E., Osindero, S. & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Comp, 18, pp. 1527-1554. doi: 10.1162/neco.2006.18.7.1527
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, 73, pp. 303-315. doi: 10.1016/j.ymssp.2015.10.025
Junbo, T., Weining, L., Junfeng, T., & Xueqian, W. (2015). Fault Diagnosis Method Study in Roller Bearing Based on Wavelet Transform and Stacked Auto-encoder. Qingdao: 27th Chinese Control and Decision Conference (CCDC) (pp. 4608-4613), May 23-25, Qingdao. doi: 10.1109/CCDC.2015.7162738
Kreyszig, E. (1979). Advanced Engineering Mathematics (4th ed.). New York, NY: John Wiley & Sons.
LeCun, Y., Bengio, Y., & Hinton, G. E. (2015). Deep Learning. Nature, 521, pp. 436-444. doi: 10.1038/nature14539
Lee, H., Largman, Y., Pham, P., & Ng, A. Y. (2009). Unsupervised feature learning for audio classification using convolutional deep belief networks. Advances in Neural Information Processing Systems, 22, pp. 1096-1104.
Liu, H., Liu, C., & Huang, Y. (2011). Adaptive feature extraction using sparse coding for machinery fault diagnosis. Mechanical Systems and Signal Processing, 25(2), pp. 558-574. doi: 10.1016/j.ymssp.2010.07.019
Martin del Campo, S., & Sandin, F. (2015). Towards zero-configuration condition monitoring based on dictionary learning. 23rd European Signal Processing Conference (EUSIPCO) (pp. 1306-1310). August 31 – September 4, Nice, France.
Møller, M. F. (1993). A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 6(4), pp. 525-533. doi: 10.1016/S0893-6080(05)80056-5
Olshausen, B. A., & Field, D. J. (1997). Sparse Coding with an Overcomplete Basis Set: A Strategy Employed by V1? Vision Research, 37(23), pp. 3311-3325. doi: 10.1016/S0042-6989(97)00169-7
Qiao, L. Q., & Xun, L. J. (2015). State of health estimation combining robust deep feature learning with support vector regression. 34th Chinese Control Conference (CCC) (pp. 6207-6212). July 28-30, Hangzhou, China. doi: 10.1109/ChiCC.2015.7260613
Rivest, R. L. (1987). Learning Decision Lists. Machine Learning, 2(3), pp. 229-246. doi: 10.1023/A:1022607331053
Rokach, L., & Maimon, O. (2005). Top-Down Induction of Decision Trees Classifiers - A Survey. IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, 35(4), pp. 476-487. doi: 10.1109/TSMCC.2004.843247
Scheffer, C., & Girdhar, P. (2004). Practical Machinery Vibration Analysis & Predictive Maintenance. Oxford, UK: Newnes.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, pp. 85-117. doi: 10.1016/j.neunet.2014.09.003
Sejdić, E., Djurović, I. & Jiang, J. (2009). Time-frequency feature representation using energy concentration: An overview of recent advances. Digital Signal Processing, 19(1), pp. 153-183. doi: 10.1016/j.dsp.2007.12.004
Shawe-Taylor, J., & Cristianini, N. (2004). Kernel Methods for Pattern Analysis. Cambridge, UK: Cambridge University Press.
Tuma, J. (2005). Setting the Passband Width in the Vold-Kalman Order Tracking Filter. 12th International Congress on Sound and Vibration (ICSV12) (pp. 1-8). July 11-14, Lisbon.
Verma, N. K., Gupta, V. K., Sharma, M., & Sevakula, R. K. (2013). Intelligent Condition Based Monitoring of Rotating Machines using Sparse Auto-encoders. Proceedings of IEEE Conference on Prognostics and Health Management (pp. 1-7). June 24-27, Gaithersburg, MD. doi: 10.1109/ICPHM.2013.6621447
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., & Manzagol, P.-A. (2010). Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. Journal of Machine Learning Research, 11, pp. 3371-3408.
van der Seijs, M. (2013) Second generation Vold-Kalman Order Filtering.
http://www.mathworks.com/matlabcentral/fileexchange /36277-second-generation-vold-kalman-order-filtering
Vold, H., & Leuridan, J. (1995). High Resolution Order Tracking at Extreme Slew Rates Using Kalman Tracking Filters. Shock and Vibration, 2(6), pp. 507-515. doi: 10.4271/931288
Wang, K., & Heyns, P. S. (17-19 June 2011). A Comparison between Two Conventional Order Tracking Techniques in Rotating Machine Diagnostics. IEEE International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (ICQR2MSE) (pp. 478-481). June 17-19, Xi'an. doi: 10.1109/ICQR2MSE.2011.5976657
Technical Papers