Fault Diagnosis and Prognosis Based on Lebesgue Sampling
Traditional fault diagnosis and prognosis (FDP) approaches are based on periodic sampling, i.e. samples are taken and algorithms are executed both in a periodic manner. As the volume of sensor data and complexity of algorithms keep increasing, the bottleneck of FDP is mainly the limited computational resources, which is especially true for distributed applications where FDP functions are deployed on microcontrollers and embedded systems with limited computation re- sources. This paper introduces the concept of Lebesgue sampling in FDP and proposes a Lebesgue sampling based fault diagnosis and prognosis (LS-FDP) framework. In the proposed LS-FDP, a novel diagnostic philosophy of “execution only when necessary” is developed in computation cost re- duction and uncertainty management. For prognosis, different from traditional approaches in which the prognostic horizon is on the time axis, the proposed approach defines prognostic horizon on the state axis. With a reduced prognostic horizon, the LS-FDP naturally benefits the uncertainty management. The goal is to create the fundamental knowledge for LS-FDP solutions that are cost-efficient, capable for the de- ployment on systems with limited computation sources, and supportive to the trend of distributed FDP schemes in complex systems. The design and implementation of LS-FDP based on particle filtering algorithms are presented with experimental results to verify the effectiveness of the proposed approaches.
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Diagnosis and fault isolation methods
Anger, C., Schrader, R., & Klingauf, U. (2012). Unscented Kalman filter with gaussian process degradation model for bearing fault prognosis. In Proceedings of the European conference of the prognostics and health management society.
Astrom, K., & Bernhardsson, B. (1999). Comparison of Riemann and Lebesgue sampling for first order stochastic systems. In Proceedings of IEEE conference on decision and control.
Balanban, E., & Slonso, J. (2013, Sept). A modeling framework for prognostic decision making and its applica- tion to uav mission planning. In Annual conference of the prognostics and health management society 2013. New Orleans, LA.
Boskoski, P., & Urevc, A. (2011). Bearing fault detection with application to PHM data challenge. International Journal of Prognostics and Health Management, 2(1), 1-10.
Celaya, J., Saxena, A., & Goebel, K. (2012). Uncertainty representation and interpretation in model-based prognostics algorithms based on Kalman filter estimation. In Proceedings of the annual conference of the prognostics and health management society. Minneapolis, MN.
Chen, C., Zhang, B., & Vachtsevanos, G. (2012). Prediction of machine health condition using neuro-fuzzy and Bayesian algorithms. IEEE Transactions on Instrumentation and Measurement, 61(2), 297-306.
Chen, C., Zhang, B., Vachtsevanos, G., & Orchard, M. (2011). Machine condition prediction based on adaptive neuro-fuzzy and high-order particle filtering. IEEE Transactions on Industrial Electronics, 58(9), 4353- 4364.
DeCastro, J., Tang, L., & Zhang, B. (2011). A safety verification approach to fault-tolerant aircraft supervisory control. In Proceedings of aiaa guidance, navigation, and control conference. Portland, OR.
de Freitas, N. (2002). Rao-Blackwellised particle filtering for fault diagnosis. In Proceedings of the IEEE aerospace conference (Vol. 4, p. 1767-1772).
Edwards, D., Orchard, M., Tang, L., Goebel, K., & Vachtsevanos, G. (2010). Impact of input uncertainty on failure prognostic algorithms: Extending the remaining useful life of
nonlinear systems. In Proceedings of the annual conference of the prognostics and health management society. Portland, OR.
Finkelstein, M. (2004). On the exponential formula for reliability. IEEE Transactions on Reliability, 53(2), 265- 268.
Goebel, K., Eklund, N., Hu, X., Avasarala, V., & Celaya, J. (2006). Defect classification of highly noisy NDE data using classifier ensembles. In Smart structures and materials. San Diego, CA.
Goebel, K., Saha, B., & Saxena, A. (2008, May). A comparison of three data-driven techniques for prognostics. In Proceedings of the 62nd meeting of the society for machinery failure prevention technology.
Hess, A., & Wells, S. (2003). Sliding mode control applied to reconfigurable flight control design. Journal of Guidance, Control,and Dynamics, 26(3), 452-462.
Huang, W., & Dietrich, D. (2005). An alternavie degradation reliability modeling approach using maximum likelihood estimation. IEEE Transactions on Reliability, 54(2), 310-317.
Isermann, R. (2005). Model-based fault detection and diagnosis-status and applications. Annual Reviews in Control, 29, 71-85.
Jardine, A., Lin, D., & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition based maintenance. Mechanical Systems and Signal Processing, 20, 1483-1510.
Kaminskiy, M. (2005). A simple procedure for Bayesian estimation of the Weibull distribution. IEEE Transactions on Reliability, 54(4), 612-616.
Lawless, J. (2003). Statistical models and methods for life- time data. Hoboken: John Wiley and Sons.
Li, Y., Kurfess, T., & Liang, S. (2000). Stochastic prognostics for rolling element bearings. Mechanical Systems and Signal Processing, 14(5), 747-762.
McCann, R., & Le, A. (2008). Lebesgue sampling with a Kalman filter in wireless sensors for smart appliance networks. In Proceedings of industry applciaitons so- ciety annual meeting.
Morales-Menendez, R., de Freitas, N., Monterrey, I., Freitas, O. D., & Poole, D. (2002). Real-time monitoring of complex industrial processes with particle filters. In Nips (pp. 1433–1440).
Oppenheimer, C. H., & Loparo, K. A. (2002). Physically based diagnosis and prognosis of cracked rotor shafts. In Aerosense (pp. 122–132).
Orchard, M., Hevia-Koch, P., Zhang, B., & Tang, L. (2013, Nov). Risk measures for particle-filtering-based state- of-charge prognosis in lithium-ion batteries. IEEE Transactions on Industrial Electronics, 60(11), 5260- 5269.
Orchard, M., & Vachtsevanos, G. (2009). A particle filtering approach for online fault diagnosis and failure progno- sis. Transactions of the Institute of Measurement and Control, 31(3/4), 221-246.
Saha,B., Goebel, K., Poll, S., & Christophersen, J. (2009). Comparison of prognostic algorithms for estimating re- maining useful life of batteries. Transactions of the In- stitute of Measurement and Control, 31(3-4), 293-308.
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2009). On application the prognostic performance metrics. In Proceedings of international conference on prognostics and health management.
Schwabacher, M., & Goebel, K. (2007). A survey of artificial intelligence for prognostics. In Aaai fall symposium (p. 107-114). LA USA.
Tang, L., Hettler, E., Zhang, B., & DeCastro, J. (2011). A testbed for real-time autonomous vehicle PHM and contingency management applications. In Proceedings of international conference on prognostics and health management. Montreal, Canada.
Tang, L., Zhang, B., DeCastro, J., & Hettler, E. (2011). An integrated health and contingency management case study on an autonomous ground robot. In Proceedings
of the 9th IEEE international conference on control and automation.
Tumer, I., & Bajwa, A. (2004). A survey of aircraft engine health monitoring systems. In Proceedings of the 35th aiaa/asme/ sae/asee joint propulsion conference (p. 620-625).
Usynin, A., & Hines, J. (2007, November). Uncertainty management in shock models applied to prognostic problems. In AAAI fall symposium. Arlington, VA.
Vachtsevanos, G., Lewis, F., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering systems. John Wiley and Sons.
Yang, G. (2005). Accelated life test at higher usage rates. IEEE Transactions on Reliability, 54(1), 53-57. Zhang, B., Khawaja, T., Patrick, R., & Vachtsevanos, G.
(2008). Blind deconvolution de-noising for helicopter vibration signals. IEEE/ASME Transactions on Mechatronics, 13(5), 558-565.
Zhang, B., Khawaja, T., Patrick, R., & Vachtsevanos, G. (2010). A novel blind deconvolution de-noise scheme in failure prognosis. Transactions of the Institute of Measurement and Control, 32(1), 3-30.
Zhang, B., Khawaja, T., Patrick, R., Vachtsevanos, G., Orchard, M., & Saxena, A. (2009). Application of blind deconvolution de-noising in failure prognosis. IEEE Transactions on Instrumentation and Measurement, 58(2), 303-310.
Zhang, B., Sconyers, C., Byington, C., Patrick, R., Orchard, M., & Vachtsevanos, G. (2009). A probabilistic fault detection approach: application to bearing fault de- tection. IEEE Transactions on Industrial Electronics, 58(5), 2011-2018.
Zhang, B., Tang, L., DeCastro, J., & Goebel, K. (2011, Aug).Prognostics-enhanced receding horizon mission planning for field autonomous vehicles. In Aiaa guidance, navigation, and control conference. Portland, OR.
Zhong, M., Fang, H., & Ye, H. (2007). Fault diagnosis of networked control system. Annual Reviews in Control, 31(1), 55-68.
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