Kalman Filter Fault Diagnosis Example

In this example, we demonstrate how to implement fault diagnosis using a Kalman Filter-based approach. The Kalman Filter is an optimal recursive algorithm for state variable estimation, widely applied in various domains including aircraft navigation and robotic control systems. For fault diagnosis applications, the Kalman Filter enables effective detection and diagnosis of system anomalies by comparing predicted system states with actual measurements. The implementation typically involves designing state-space models, configuring process and measurement noise covariance matrices (Q and R), and implementing residual analysis for fault detection. Through proper Kalman Filter implementation, we can accurately estimate system states while monitoring for potential faults using innovation sequences or parameter estimation techniques. This example provides beginners with practical MATLAB code structures for implementing prediction-update cycles, including key functions like 'kalmandesign' for filter synthesis and real-time monitoring logic for fault threshold detection.