A Comparison of Methods for Linear Cell-to-Cell Mapping and Application Example for Fault Detection and Isolation

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Jul 8, 2014
Sara Mohon Pierluigi Pisu

Abstract

In this paper, the Generalized Cell Mapping (GCM) method for a linear system is compared with a new stochastic method for novel cell-to-cell mapping. The authors presented the new stochastic method in a previous paper last year. The two methods are compared in an application example of a vehicle alternator. The alternator may experience three faults including belt slippage, a broken
diode, or incorrect controller reference voltage. Fault detection and isolation (FDI) is performed using the two cell-to-cell mapping methods. The results show that the new stochastic method is more computationally intensive but yields better isolation results than the GCM method

How to Cite

Mohon, S., & Pisu, P. (2014). A Comparison of Methods for Linear Cell-to-Cell Mapping and Application Example for Fault Detection and Isolation. PHM Society European Conference, 2(1). https://doi.org/10.36001/phme.2014.v2i1.1484
Abstract 47 | PDF Downloads 62

##plugins.themes.bootstrap3.article.details##

Keywords

Monte Carlo, Diagnosis and fault isolation methods, generalized cell mapping, cell-to-cell mapping

References
Chiu, H. M. & Hsu, C. S. (1986). A Cell Mapping Method
for Nonlinear Deterministic and Stochastic Systems—
Part II: Examples of Application. ASME Journal of
Applied Mechanics. September Vol. 53. pp. 702-710.
Doi: 10.1115/1.3171834
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
Hsu, C. S. (1980). A Theory of Cell-to-Cell Mapping
Dynamical Systems. ASME Journal of Applied
Mechanics. December Vol. 47. pp. 931-939.
Doi:10.1115/1.3153816
Hsu, C. S. & Guttalu, R. S. (1980). An Unravelling
Algorithm for Global Analysis of Dynamical Systems:
An Application to Cell-to-Cell Mappings. ASME
Journal of Applied Mechanics. December Vol. 47. pp.
940-948. Doi: 10.1115/1.3153817
Hsu, C. S. (1981). A Generalized Theory of Cell-to-Cell
Mapping for Nonlinear Dynamical Systems. ASME
Journal of Applied Mechanics. September Vol. 48. pp.
634-642. Doi: 10.1115/1.3157686
Hsu, C. S. (1982). A Probabilistic Theory of Nonlinear
Dynamical Systems Based on the Cell State Space
Concept. ASME Journal of Applied Mechanics.
December Vol. 49. pp. 895-902. Doi:
10.1115/1.3162633
Hsu, C. S. & Chiu, H. M. (1986). A Cell Mapping Method
for Nonlinear Deterministic and Stochastic Systems—
Part I: The Method of Analysis. ASME Journal of
Applied Mechanics. September Vol. 53. pp. 695-701.
Doi: 10.1115/1.3171833
Hsu, C. S., John F., Marsden, J. E., & Sirovich, L. (Eds.).
(1987). Cell-to-Cell Mapping: A Method for Global
Analysis for Nonlinear Systems. New York: Springer.
Kastner, M. (2010). Monte Carlo Methods in Statistical
Physics: Mathematical Foundations and Strategies.
Communications in Nonlinear Science and Numerical
Simulation. June Vol. 15. pp. 1589-1602. Doi:
10.1016/j.cnsns.2009.06.011
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 Prognostics and Health Management
Society, October 14-17, New Orleans, Louisiana.
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
Sobol, I. (1994). A Primer for the Monte Carlo Method.
Boca Raton: CRC Press.
Wang, J. (1999). Transition Matrix Monte Carlo Method.
Proceedings of the Europhysics Conference on
Computational Physics (CCP). September Vol. 121.
pp. 22-25. Doi: 10.1016/S0010-4655(99)00270-2
Section
Technical Papers