A Stochastic Modeling Approach of Quantized Systems with Application to Fault Detection and Isolation of an Automotive Electrical Power Generation Storage System
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
This paper introduces a stochastic modeling approach for a quantized system for the purpose of fault detection and isolation in an automotive alternator system. Three common alternator faults including belt slip, diode failure, and incorrect reference voltage for the voltage controller are considered and analyzed. A continuous nonlinear model of the alternator system is quantized into discrete states in order to simplify diagnostic efforts. The paper describes a stochastic modeling approach that creates a time-varying probability transition matrix that can be computed in real- time without the need for Monte Carlo simulation. Fault detection and isolation occurs through comparison of the most probable state from the transition matrix and the quantized output state.
How to Cite
##plugins.themes.bootstrap3.article.details##
Diagnosis and fault isolation methods, residual generation, alternator, probability transition matrix, electrical power generation storage system
Blanke, M., Kinnaert, M., Lunze, J., & Staroswiecki, M. (2006). Diagnosis and Fault-Tolerant Control. Germany: Springer.
Hashemi, A., & Pisu, P. (2011). Adaptive Threshold-based Fault Detection and Isolation for Automotive Electrical Systems (pp. 1013-1018), World Congress on Intelligent Control and
Automation. June 21-25, Taipei, Taiwan. doi: 10.1109/WCICA.2011.5970668
Hashemi, A., & Pisu, P. (2011). Fault Diagnosis in Automotive Alternator System Utilizing Adaptive Threshold Method. Annual Conference of Prognostics and Health Management Society. September 25-29, Montreal, Canada.
Hashemi, A., (2011). Model-Based System Fault Diagnosis Utilizing Adaptive Threshold with Application to Automotive Electrical Systems. Masters dissertation. Clemson University, Clemson, South Carolina, USA. http://etd.lib.clemson.edu/documents/1314212419/Hash emi_clemson_0050M_11327.pdf
Scacchioli, A., Rizzoni, G., & Pisu, P., (2006). Model- Based Fault Detection and Isolation in Automotive Electrical Systems, ASME International Mechanical Engineering Congress and Exposition (pp. 315-324), November 5-10, Chicago, Illinois, USA. doi: 10.1115/IMECE2006-14504
Scacchioli, A., Li, W., Suozzo, C., Rizzoni, G., Pisu, P., Onori, S., Salman, M., Zhang, X., (2010). Experimental Implementation of a Model-Based Diagnosis for an electric Power Generation and Storage Automotive System, Submitted ASME Tran. Dynamic Systems, Measurement, and Control.
Scacchioli, A., Rizzoni, G., Salman, M., Onori, S., & Zhang, X. (2013). Model-based Diagnosis of an Automotive Electric Power Generation and Storage System. IEEE Transaction on
Human Machine Systems (pp. 1-14), March 7. doi: 10.1109/TSMCC.2012.2235951
Zhang, X., Uliyar, H., Farfan-Ramos, L., Zhang, Y., & Salman, M. (2010). Fault Diagnosis of Automotive Electric Power Generation and Storage Systems, IEEE International Conference on Control Applications (pp. 719-724), September 8-10, Yokohama, Japan. doi: 10.1109/CCA.2010.5611179
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.