System Independent Fault Diagnosis for Synchronous Generator



Published Nov 16, 2020
Jeet Gandhi R. Gopinath C. Santhosh Kumar


Creating a unified fault diagnosis model that can detect faults across systems with different ratings (system independent fault diagnosis) would be of great interest in making condition-based maintenance (CBM) more popular. In this work, three phase synchronous generators with 3 and 5 kVA ratings are used for detecting stator inter-turn short circuit faults.
Our baseline is a 3 kVA generator working at 1 A load during training and testing, to emulate the system/load dependent fault diagnosis. We obtained a classification accuracy of 99.75%, 100% and 100% for R phase, Y phase and B phase faults respectively. Subsequently, we evaluated the system for its load independent performance. Performance accuracy deteriorated due to the load specific variations (LSV) in the input feature vector (IFV). LSV is undesired, and we used nuisance attribute projection (NAP) to remove them. Using NAP, we obtained a performance improvement of 23.13%, 17.75% and 20.72% for three fault models on the 3 kVA generator and similar performance improvement was obtained for 5 kVA generator also.
Further, we experimented for load and system independent fault diagnosis. In this case, we consider LSV and system specific variations (SSV) on IFV as undesired. We experimented with two types of NAP, (1) single step NAP, (2) stacked NAP. Experimental results show that the two staged stacked NAP outperforms. We obtained an improvement of 23.99%, 16.06% and 28.39%, in classification accuracy for three fault models, resulting in overall classification accuracy of 89.22%, 94.67% and 94.59% for R phase, Y phase and B phase fault models respectively.

Abstract 204 | PDF Downloads 184



Machine fault diagnosis, Synchronous generator, Support Vector Machine, Discrete wavelet transform, Inter-turn fault, Nuisance attribute projection

Awadallah, M. A., & Morcos, M. M. (2003). Application of ai tools in fault diagnosis of electrical machines and drives-an overview. IEEE Transactions on energy conversion, 18(2), 245–251.
Bendjama, H., Bouhouche, S., & Boucherit, M. S. (2012). Application of wavelet transform for fault diagnosis in rotating machinery. International Journal of Machine Learning and Computing, 2(1), 82.
Bessam, B., Menacer, A., Boumehraz, M., & Cherif, H. (2017). Wavelet transform and neural network techniques for inter-turn short circuit diagnosis and location in induction motor. International Journal of System Assurance Engineering and Management, 8(1), 478–488.
Dash, R. N., Subudhi, B., & Das, S. (2010). Induction motor stator inter-turn fault detection using wavelet transform technique. In Industrial and information systems (iciis), 2010 international conference on (pp. 436–441).
Daubechies, I. (1988). Orthonormal bases of compactly supported wavelets. Communications on pure and applied mathematics, 41(7), 909–996.
Eris¸ti, H., Uc¸ar, A., & Demir, Y. (2010). Wavelet-based feature extraction and selection for classification of power system disturbances using support vector machines. Electric power systems research, 80(7), 743–752.
Gandhi, A., Corrigan, T., & Parsa, L. (2011). Recent advances in modeling and online detection of stator interturn faults in electrical motors. IEEE Transactions on Industrial Electronics, 58(5), 1564–1575.
Gandhi, J., Gopinath, R., & Kumar, C. S. (2017). Nuisance attribute projection for system independent fault diagnosis of synchronous generator. In Wireless communication, signal processing and networking (wispnet), 2017 international conference on (pp. 563–568).
Gopinath, R., Kumar, C. S., & Ramachandran, K. (2016). Scalable fault models for diagnosis of synchronous generators. International Journal of Intelligent Systems Technologies and Applications, 15(1), 35–51.
Gopinath, R., Kumar, C. S., Ramachandran, K., Upendranath, V., & Kiran, P. S. (2016). Intelligent fault diagnosis of synchronous generators. Expert Systems with Applications, 45, 142–149.
Gopinath, R., Kumar, C. S., Vishnuprasad, K., & Ramachandran, K. (2015). Feature mapping techniques for improving the performance of fault diagnosis of synchronous generator. International Journal of Prognostics and Health Management, 6(2), 12.
Gopinath, R., Nambiar, T., Abhishek, S., Pramodh, S. M., Pushparajan, M., Ramachandran, K., . . . Thirugnanam, R. (2013). Fault injection capable synchronous generator for condition based maintenance. In Intelligent systems and control (isco), 2013 7th international conference on (pp. 60–64).
Jardine, A. K., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical systems and signal processing, 20(7), 1483–1510.
Krishna, V., Chendur, P. P., Abhilash, P., Abraham, R. T., Gopinath, R., & Kumar, C. S. (2016). Improving the performance of wavelet based machine fault diagnosis system using locality constrained linear coding. In The international symposium on intelligent systems technologies and applications (pp. 951–964).
Nandi, S., Toliyat, H. A., & Li, X. (2005). Condition monitoring and fault diagnosis of electrical motorsa review. IEEE transactions on energy conversion, 20(4), 719–729.
Neti, P., & Nandi, S. (2009). Stator interturn fault detection of synchronous machines using field current and rotor search-coil voltage signature analysis. IEEE Transactions on Industry Applications, 45(3), 911–920.
Rajeswari, R., & Kamaraj, N. (2007). Diagnosis of inter turn fault in the stator of synchronous generator using wavelet based anfis. World Academy of Science, Engineering and Technology, 36, 203–209.
Siddique, A., Yadava, G., & Singh, B. (2005). A review of stator fault monitoring techniques of induction motors. IEEE transactions on energy conversion, 20(1), 106–114.
Solomonoff, A., Campbell, W. M., & Boardman, I. (2005). Advances in channel compensation for svm speaker recognition. In Acoustics, speech, and signal processing, 2005. proceedings.(icassp’05). ieee international conference on (Vol. 1, pp. I–629).
Solomonoff, A., Campbell, W. M., & Quillen, C. (2007). Nuisance attribute projection. Speech Communication, 1–73.
Struc, V., Vesnicer, B., Mihelič, F., & Pavešič, N. (2010). Removing illumination artifacts from face images using the nuisance attribute projection. In Acoustics speech and signal processing (icassp), 2010 ieee international conference on (pp. 846–849).
Tome, P., Vera-Rodriguez, R., Fierrez, J., & Ortega-García, J. (2012). Variability compensation using nap for unconstrained face recognition. In Distributed computing and artificial intelligence (pp. 129–139). Springer.
Wang, C., Liu, X., & Chen, Z. (2014). Incipient stator insulation fault detection of permanent magnet synchronous wind generators based on hilbert–huang transformation. IEEE Transactions on Magnetics, 50(11), 1–4.
Wu, J.-D., & Liu, C.-H. (2008). Investigation of engine fault diagnosis using discrete wavelet transform and neural network. Expert Systems with Applications, 35(3), 1200–1213.
Yan, R., Gao, R. X., & Chen, X. (2014). Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing, 96, 1–15.
Yifrach, A., Novoselsky, E., Solewicz, Y. A., & Yitzhaky, Y. (2016). Improved nuisance attribute projection for face recognition. Pattern Analysis and Applications, 19(1), 69–78.
Yin, S., Ding, S. X., Xie, X., & Luo, H. (2014). A review on basic data-driven approaches for industrial process monitoring. IEEE Transactions on Industrial Electronics, 61(11), 6418–6428.
Technical Papers