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
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
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