The paper illustrates the design and verification of a model-based diagnostic algorithm for fault detection and isolation of a Diesel Particulate Filter (DPF). The algorithm relies on the Structural Analysis theory, which identifies proper fault indicators (i.e., residuals) upon the knowledge of the mathematical relationships within the system model. The main innovation of the proposed work consists in applying for the first time such theory to a Diesel after-treatment system. To check theoretical fault detectability and isolability, the approach requires only qualitative correlations among the physical variables and parameters representative of the faults that may occur into the system, without the need for quantitative information. Nevertheless, to build the residuals, the form of the equations needs to be defined and properly implemented. The mathematical model developed in this work is a lumped dynamic model that accounts for two main control volumes, one related to the Diesel Oxidation Catalyst (DOC) and the other to the DPF. Mass and energy balance equations are introduced in both volumes. Within the DPF, soot inlet flow is related to the engine exhaust flow, and its oxidation kinetics is modelled by means of Arrhenius-type equation. Four faulty states are introduced into this study with respect to: i) variation in fuel-post injection amount (i.e., injector malfunction), ii) partial or missed fuel combustion in the DOC, iii) partial or missed residual fuel combustion in the DPF, and iv) degradation of the substrate viscosity. Each faulty state is modelled through a dedicated parameter and the overall model is then used to perform faults isolability through Structural Analysis. The theoretical results show that the proposed diagnostic algorithm can detect and correctly isolate all the considered faults. Then, residuals are built and verified in simulated environment on specific cases, proving the capability of the algorithm to successfully detect and isolate the considered faults.
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