Particle Filter-Based Fault Detection

This paper presents a particle filter-based fault detection method utilizing state estimation and residual smoothing techniques for effective identification of various system faults. Particle filtering operates through sequential Monte Carlo methods, implementing prediction and update stages where system states are represented by weighted particles. Key functions typically include:

- State prediction using system dynamics models - Weight calculation based on measurement likelihood - Residual generation through comparison between observed and estimated outputs

The state estimation component employs Bayesian filtering principles, where code implementations often feature importance sampling and resampling algorithms to maintain particle diversity. Residual smoothing enhances detection accuracy by applying moving average filters or exponential smoothing to noise-reduce the residual signals. The method demonstrates practical value for engineering applications, particularly in nonlinear systems where traditional Kalman filters may underperform. Implementation typically requires 50-200 particles depending on system complexity, with adaptive particle number strategies often coded for computational efficiency.