Improvements to Single-Fault Isolation Using Estimated Parameters

A method for finer fault isolation or localization in the model based fault detection and isolation (FDI) paradigm is developed using parallely computed bond graph models. Many of the existing model based FDI methods are based on the evaluation of model consistency expressed in terms of analytical redundancy relations (ARR). These evaluations lead to residuals, and a number of sensors are to be installed in the plant to generate independent signatures needed for fault isolation. However, all the possible faults may not be isolable with the available instrumentation and it is sometimes expensive or technically impossible to install necessary sensors in the plant to physically measure each and every state. In such situations, all components faults may not be uniquely isolated. However, a unique fault parameter subspace can be identified. One of the possible solutions as proposed in this paper is to estimate parameters of that subspace from the ARR by assuming a single fault hypothesis and then to incorporate the estimated values in separate models to run parallely with the plant during the fault. Thereafter, comparison of model behaviors leads to localization of the faulty parameters. This method is applied to an example system.