In model-based quantitative multiple fault detection and isolation (FDI), fault disambiguation is based on parameter estimation. In this paper, the fault hypothesis is generated by evaluating a set of analytical redundancy relations (ARRs) and parameter values corresponding to the unstructured part of the fault subspace are estimated by minimizing a function of the ARRs. Process and measurement uncertainties are handled by using a passive approach for robust FDI. Bond graph modelling is used to describe process models and to derive the ARRs. The bond graph model of the process is differentially causalled and it is then converted into a diagnostic bond graph form. The diagnostic bond graph is further converted into its corresponding sensitivity bond graph form, which gives the residual sensitivity to parametric changes. The developed algorithm provides quicker fault isolation because only a few parameters are estimated and it does not need several model simulations, thereby making it suitable for real-time process supervision.
A lv Port opening area of the orifice of the unloading DC valve (m 2) A port Port opening area of the port of the soft switch (m 2) A P_x Port opening area of the DCV (m 2) A ss Area of the piston of the soft switch (m 2) A v Valve orifice area (m 2) C Single port energy storage capacitor element in bond graph model C d Coefficient of discharge C D Coefficient of flow through check valve C ss Radial clearance of the piston of the soft switch (µm) De Effort dictator element in bond graph model Df Flow dictator element in bond graph model D m Volume displacement rate of the hydro-motor (m 3 rad −1) D p Volume displacement rate of the radial piston pump (m 3 rad −1) e Effort in bond graph model E throtloss Throttling energy loss (J) f Flow in bond graph model F preload Preload of the spring of the soft switch (N) I Single port energy storage inertial element in bond graph model J Load inertia of the rotating shaft of the hydro-motor (kg m 2) K Bulk modulus of air free flowing fluid (N m −2
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