A Field Programmable Gate Array (FPGA) Based Non-Linear Filters for Gas Turbine Prognostics
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Published
Apr 27, 2021
Jayant Kumar Nayak
Vatsala Prasad
Ranjan Ganguli
Abstract
The removal of noise from signals obtained through the health monitoring systems in gas turbines is an important consideration for accurate prognostics. Several filters have been designed and tested for this purpose, and their performance analysis has been conducted. Linear filters are inefficient in the removal of outliers and noise because they cause smoothening of the sharp features in the signal which can indicate the onset of a fault event. On the other hand, non-linear filters based on image processing methods can provide more precise results for gas turbine health signals. Among others, the weighted recursive median (WRM) filter has been shown to provide greater accuracy due to its weight adaptability depending on the signal type. However, sampling data at high rates is possible which needs hardware implementation of the filter. In this paper, the design, simulation and implementation of WRM filters on the FPGA (Field Programmable Gate Arrays) platforms Vivado Design Suite by Xilinx and Quartus Pro Lite Edition 19.3 has been performed. The architectural detail and performance result with the FPGA filters when subjected to abrupt and gradual fault signal is presented.
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Keywords
Field Programmable Gate Array, Gas Turbine, Prognostics
References
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Zhao N., Wen, X., & Li, S. (2016). A Review on gas turbine anomaly detection for implementing health management Volume 1: Aircraft Engine; Fans and Blowers; Marine. doi: 10.1115/GT2016-58135
Fentaye A., Baheta A., Gilani S., & Kyprianidis K. (2019). A review on gas turbine gas-path diagnostics: State-of-the-art methods, challenges and opportunities Aerospace, 6(7), 83 doi: 10.3390/aerospace6070083
Ganguli R. (2012) Jet engine gas-path measurement filtering using center weighted idempotent median filters, Journal of Propulsion and Power Vol. 19 Issue 5. doi: 10.2514/2.6186
Luppold R., Roman J., Gallops G., & Kerr L. (1989). Estimating in-flight engine performance variations using kalman filter concepts. 25th Joint Propulsion Conference. doi: 10.2514/6.1989-2584
Pourbabaee B., Meskin N., & Khorasani K. (2016). Sensor Fault Detection, Isolation, and Identification Using Multiple Model Based Hybrid Kalman Filter for Gas Turbine Engines. IEEE Transactions on Control Systems Technology, Vol. 24, Issue 4 doi: 10.1109/TCST.2015.2480003
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Lu F., Ju H., & Huang J. (2016). An improved extended Kalman filter with inequality constraints for gas turbine engine health monitoring Aerospace Science and Technology, Vol. 58, 36–47. doi: 10.1016/j.ast.2016.08.008
Daroogheh N., Baniamerian A., Meskin N., & Khorasani K. (2015) A hybrid prognosis and health monitoring strategy by integrating particle filters and neural networks for gas turbine engines IEEE Conference on Prognostics and Health Management(PHM) doi: 10.1109/ICPHM.2015.7245020
Liu, J., Liu, J., Yu, D., Kang, M., Yan, W., Wang, Z., & Pecht, M. (2018) Fault detection for gas turbine hot components based on a convolutional neural network Energies, Vol 11 Issue 8. doi: 10.3390/en11082149
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Y. G. Li , Ghafir A., Wang L., Singh R., Huang K. & Feng X.(2011). Nonlinear multiple points gas turbine off-design performance adaptation using a genetic algorithm, J. Eng. Gas Turbines Power, Vol. 133 Issue 7. doi: 10.1115/1.4002620
Li, Y. G., Abdul Ghafir, M. F., Wang, L., Singh, R., Huang, K., Feng, X., & Zhang, W. (2012). Improved multiple point nonlinear genetic algorithm based performance adaptation using least square method, J. Eng. Gas Turbines Power, Vol 134 Issue 3. doi: 10.1115/1.4004395
Henaut J., Dragomirescu D., & Plana R. (2009). FPGA based high date rate radio interfaces for aerospace wireless sensor systems 2009 Fourth International Conference on Systems doi: 10.1109/ICONS.2009.28
Fay D., Campbell S., Miller G., & Connors D. (2007). Teaching fault tolerant FPGA design for aerospace applications. 2007 IEEE International Conference on Microelectronic Systems Education (MSE’07). doi: 10.1109/MSE.2007.81
Gankidi P. R., & Thangavelautham J. (2017). FPGA architecture for deep learning and its application to planetary robotics. 2017 IEEE Aerospace Conference doi: 10.1109/AERO.2017.7943929
Verma R., & Ganguli R. (2005). Denoising jet engine gas path measurements using nonlinear filters IEEE/ASME Transactions on Mechatronics, Vol. 10 Issue 4. doi: 10.1109/TMECH.2005.852454
Payuna U. & Ganguli R., (2010) Jet engine health signal denoising using optimally weighted recursive median filters J. Eng. Gas Turb. Power vol. 132 Issue 4 doi: 10.1115/1.3200907
Hore A & Ziou D. (2010) Image quality metrics: PSNR vs. SSIM. 20th International Conference on, pp. 2366–2369. IEEE Pattern Recognition (ICPR), 2010. doi: 10.1109/ICPR.2010.579
Saladi S., & Prabha, N. (2017). Analysis of denoising filters on MRI brain images. International Journal of Imaging Systems and Technology, vol.27 Issue 3 doi: 10.1002/ima.22225
Turner K. & Bajwa A. (1999) A survey of aircraft engine health monitoring systems AIAA/ ASME/ SAE/ ASEE Joint Propulsion Conference and Exhibit 20-24 June 1999 doi: 10.2514/6.1999-2528
Fahmy S.A, Cheung P. & Luk W. (2009) High-throughput one-dimensional median and weighted median filters on FPGA IET Computers & Digital Techniques 2009 doi: 10.1049/iet-cdt.2008.0119
Benkrid K., Crookes D., & Benkrid A. (n.d.). (2002) Design and implementation of a novel algorithm for general purpose median filtering on FPGAs 2002 IEEE International Symposium on Circuits and Systems. doi: 10.1109/ISCAS.2002.1010482
Hyeong-Seok Yu, Joon-Yeop Lee, & Jun-Dong Cho (1999) A fast VLSI implementation of sorting algorithm for standard median filters Twelfth Annual IEEE International ASIC/SOC Conference doi: 10.1109/ASIC.1999.806540
HajiRassouliha, A., Taberner, A. J., Nash, M. P., & Nielsen, P. M. F. (2018). Suitability of recent. hardware accelerators (DSPs, FPGAs, and GPUs) for computer vision and image processing algorithms. Signal Processing: Image Communication, 68, 101–119. doi:10.1016/j.image.2018.07.007
Volponi, A. J., DePold, H., Ganguli, R., & Daguang, C. (2000). The Use of Kalman Filter and Neural Network Methodologies in Gas Turbine Performance Diagnostics: A Comparative Study. Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education. doi:10.1115/2000-gt-0547
Krizhevsky A., Sutskever I., Hinton G.E. (2012) ImageNet Classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst., 2012, pp. 1–9.
Caulfield A. M., Chung E.S, Putnam A., Angepat H., Fowers J., Haselman M., Heil S., Humphrey M., Kaur P., Kim J.Y, Lo D., Massengill T., Ovtcharov K., Papamichael M., Woods L., Lanka S., Chiou D. and Burger D. (2016) A cloud-scale acceleration architecture, 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), Taipei, 2016, pp. 1-13, doi: 10.1109/MICRO.2016.7783710.
Borguet S., Henriksson M., McKelvey T. & Léonard O. (2011). A study on engine health monitoring in the frequency domain, J. Eng. Gas Turbines Power., Vol. 133 Issue 8 doi: 10.1115/1.4002832
Zhao N., Wen, X., & Li, S. (2016). A Review on gas turbine anomaly detection for implementing health management Volume 1: Aircraft Engine; Fans and Blowers; Marine. doi: 10.1115/GT2016-58135
Fentaye A., Baheta A., Gilani S., & Kyprianidis K. (2019). A review on gas turbine gas-path diagnostics: State-of-the-art methods, challenges and opportunities Aerospace, 6(7), 83 doi: 10.3390/aerospace6070083
Ganguli R. (2012) Jet engine gas-path measurement filtering using center weighted idempotent median filters, Journal of Propulsion and Power Vol. 19 Issue 5. doi: 10.2514/2.6186
Luppold R., Roman J., Gallops G., & Kerr L. (1989). Estimating in-flight engine performance variations using kalman filter concepts. 25th Joint Propulsion Conference. doi: 10.2514/6.1989-2584
Pourbabaee B., Meskin N., & Khorasani K. (2016). Sensor Fault Detection, Isolation, and Identification Using Multiple Model Based Hybrid Kalman Filter for Gas Turbine Engines. IEEE Transactions on Control Systems Technology, Vol. 24, Issue 4 doi: 10.1109/TCST.2015.2480003
Lu F., Huang Y., Huang J., & Qiu X. (2018). A Hybrid Kalman Filtering Approach Based on Federated Framework for Gas Turbine Engine Health Monitoring IEEE Access: The Multidisciplinary Open Access Journal, Vol 6. doi: 10.1109/ACCESS.2017.2780278
Lu F., Ju H., & Huang J. (2016). An improved extended Kalman filter with inequality constraints for gas turbine engine health monitoring Aerospace Science and Technology, Vol. 58, 36–47. doi: 10.1016/j.ast.2016.08.008
Daroogheh N., Baniamerian A., Meskin N., & Khorasani K. (2015) A hybrid prognosis and health monitoring strategy by integrating particle filters and neural networks for gas turbine engines IEEE Conference on Prognostics and Health Management(PHM) doi: 10.1109/ICPHM.2015.7245020
Liu, J., Liu, J., Yu, D., Kang, M., Yan, W., Wang, Z., & Pecht, M. (2018) Fault detection for gas turbine hot components based on a convolutional neural network Energies, Vol 11 Issue 8. doi: 10.3390/en11082149
Tayarani-Bathaie, S. S., Vanini, Z. N. S., & Khorasani K. (2012). Fault detection of gas turbine engines using dynamic neural networks. 2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) doi: 10.1109/CCECE.2012.6334837
Y. G. Li , Ghafir A., Wang L., Singh R., Huang K. & Feng X.(2011). Nonlinear multiple points gas turbine off-design performance adaptation using a genetic algorithm, J. Eng. Gas Turbines Power, Vol. 133 Issue 7. doi: 10.1115/1.4002620
Li, Y. G., Abdul Ghafir, M. F., Wang, L., Singh, R., Huang, K., Feng, X., & Zhang, W. (2012). Improved multiple point nonlinear genetic algorithm based performance adaptation using least square method, J. Eng. Gas Turbines Power, Vol 134 Issue 3. doi: 10.1115/1.4004395
Henaut J., Dragomirescu D., & Plana R. (2009). FPGA based high date rate radio interfaces for aerospace wireless sensor systems 2009 Fourth International Conference on Systems doi: 10.1109/ICONS.2009.28
Fay D., Campbell S., Miller G., & Connors D. (2007). Teaching fault tolerant FPGA design for aerospace applications. 2007 IEEE International Conference on Microelectronic Systems Education (MSE’07). doi: 10.1109/MSE.2007.81
Gankidi P. R., & Thangavelautham J. (2017). FPGA architecture for deep learning and its application to planetary robotics. 2017 IEEE Aerospace Conference doi: 10.1109/AERO.2017.7943929
Verma R., & Ganguli R. (2005). Denoising jet engine gas path measurements using nonlinear filters IEEE/ASME Transactions on Mechatronics, Vol. 10 Issue 4. doi: 10.1109/TMECH.2005.852454
Payuna U. & Ganguli R., (2010) Jet engine health signal denoising using optimally weighted recursive median filters J. Eng. Gas Turb. Power vol. 132 Issue 4 doi: 10.1115/1.3200907
Hore A & Ziou D. (2010) Image quality metrics: PSNR vs. SSIM. 20th International Conference on, pp. 2366–2369. IEEE Pattern Recognition (ICPR), 2010. doi: 10.1109/ICPR.2010.579
Saladi S., & Prabha, N. (2017). Analysis of denoising filters on MRI brain images. International Journal of Imaging Systems and Technology, vol.27 Issue 3 doi: 10.1002/ima.22225
Turner K. & Bajwa A. (1999) A survey of aircraft engine health monitoring systems AIAA/ ASME/ SAE/ ASEE Joint Propulsion Conference and Exhibit 20-24 June 1999 doi: 10.2514/6.1999-2528
Fahmy S.A, Cheung P. & Luk W. (2009) High-throughput one-dimensional median and weighted median filters on FPGA IET Computers & Digital Techniques 2009 doi: 10.1049/iet-cdt.2008.0119
Benkrid K., Crookes D., & Benkrid A. (n.d.). (2002) Design and implementation of a novel algorithm for general purpose median filtering on FPGAs 2002 IEEE International Symposium on Circuits and Systems. doi: 10.1109/ISCAS.2002.1010482
Hyeong-Seok Yu, Joon-Yeop Lee, & Jun-Dong Cho (1999) A fast VLSI implementation of sorting algorithm for standard median filters Twelfth Annual IEEE International ASIC/SOC Conference doi: 10.1109/ASIC.1999.806540
HajiRassouliha, A., Taberner, A. J., Nash, M. P., & Nielsen, P. M. F. (2018). Suitability of recent. hardware accelerators (DSPs, FPGAs, and GPUs) for computer vision and image processing algorithms. Signal Processing: Image Communication, 68, 101–119. doi:10.1016/j.image.2018.07.007
Volponi, A. J., DePold, H., Ganguli, R., & Daguang, C. (2000). The Use of Kalman Filter and Neural Network Methodologies in Gas Turbine Performance Diagnostics: A Comparative Study. Volume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education. doi:10.1115/2000-gt-0547
Krizhevsky A., Sutskever I., Hinton G.E. (2012) ImageNet Classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst., 2012, pp. 1–9.
Caulfield A. M., Chung E.S, Putnam A., Angepat H., Fowers J., Haselman M., Heil S., Humphrey M., Kaur P., Kim J.Y, Lo D., Massengill T., Ovtcharov K., Papamichael M., Woods L., Lanka S., Chiou D. and Burger D. (2016) A cloud-scale acceleration architecture, 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), Taipei, 2016, pp. 1-13, doi: 10.1109/MICRO.2016.7783710.
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Technical Papers