A Stochastic Modeling Approach of Quantized Systems with Application to Fault Detection and Isolation of an Automotive Electrical Power Generation Storage System

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Published Oct 14, 2013
Sara Mohon Pierluigi Pisu

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

Mohon, S. ., & Pisu, P. . (2013). A Stochastic Modeling Approach of Quantized Systems with Application to Fault Detection and Isolation of an Automotive Electrical Power Generation Storage System. Annual Conference of the PHM Society, 5(1). https://doi.org/10.36001/phmconf.2013.v5i1.2225
Abstract 210 | PDF Downloads 147

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Keywords

Diagnosis and fault isolation methods, residual generation, alternator, probability transition matrix, electrical power generation storage system

References
Alam, M. & Lundstrom, M. (1995). Transition Matrix Approach for Monte Carlo Simulation of Coupled Electron/Phonon/Photon Dynamics, Applied Physics Letters (Vol 67. No. 4 pp. 512-514), July 24. doi: 10.1063/1.114553.

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
Section
Technical Research Papers

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