Model Based Approach for Fault Detection and Prediction Using Particle Filters

Fault detection and failure prediction for nonlinear non-Gaussian systems is an important issue both from the economic and safety point of view. Most of the fault detection techniques assume the system model to be linear and the noise to be Gaussian. These linearization assumptions tend to suffer form poor detection and imprecise prediction. Also, they may lead to false alarms which would incur unnecessary economic expenditure. This thesis aims at using particle filter approach for fault detection and failure prediction in nonlinear non-Gaussian systems. A major advantage of this approach is that the complete probability distribution information of the state estimates from particle filter is utilized for fault detection and failure prediction. Particle filtering methods represent and recursively generate an approximation of the posterior state probability density function. They are Sequential Monte Carlo Methods based on point mass representation of probability densities, which have been applied to the Vertical Take Off and Landing (VTOL) aircraft model and DC motor model in this thesis. Two variants of particle filters: Sequential Importance Sampling Algorithm and Sequential Importance Resampling Algorithm have been studied. Sequential Importance Sampling algorithm suffers from degeneracy problem because of which Sequential Importance Resampling technique is preferred. The system is represented in state space format and the estimates are made according to the Sequential Importance Resampling algorithm. The decision rule for fault detection is evaluated using the likelihood of the estimation parameter over a sliding window. The threshold values for fault detection are set using a heuristic approach. A fault is said to be detected if the likelihood exceeds the expected threshold value. A p-step ahead prediction is done for the DC motor model after the fault has been detected, which is utilized to determine the remaining useful life of the model.