Integrated Diagnosis and Prognosis of Uncertain System: A Bond Graph Approach

Mayank Shekhar JHA, Geneviève DAUPHIN-TANGUY, and Belkacem OULD-BOUAMAMA
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phmce_14_029.pdf538.4 KBJune 13, 2014 - 6:32am

Bond Graph (BG) methodology is used to model the dynamic uncertain systems. Uncertainty is considered on the system parameters in form of intervals. The uncertain parameters are allowed to deviate within their prescribed interval limits. Single fault hypothesis is considered in this work such that the parameter undergoing degradation is known a priori. A new method for generation of interval valued thresholds is briefly described in the framework of BG models in Linear Fractional Transformation form. The diagnostic module is formed using such thresholds which detect the beginning of degradation of a parameter in the real system. The new concept of Interval Extension of Analytical Redundancy Relations (IE-ARRs) is introduced which consider the parametric uncertainties and the evolution of degrading parameter in real time. Then, the Center and Range method for fitting linear regression models to interval symbolic data is adapted to fit piece wise linear models to the interval valued times series data of IE-ARRs. Further, the new concept of generation of failure thresholds from a nominal system model is introduced and developed. Finally, the fitted linear model is used to estimate the remaining useful life of the parameter under degradation. Simulations are carried out on an example DC motor model. Linear and non-linear parametric degradations are considered. Results are presented in form of simulations.

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Submission Keywords: 
model uncertainty
Parameter interval
failure prognosis
integrated system health management
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
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