SIR Particle Filter-Based Fault Diagnosis Method with Algorithm Implementation

In the field of mechanical fault diagnosis, the SIR (Sequential Importance Resampling) particle filter-based fault diagnosis method has been extensively researched and applied. This approach leverages the advantages of particle filters to effectively detect and diagnose faults through sequential Monte Carlo methods. The implementation typically involves generating weighted particles that represent possible system states, with resampling steps to mitigate degeneracy issues. Key performance parameters include false alarm rate (type I errors) and miss detection rate (type II errors), which significantly impact the accuracy and reliability of diagnostic results. When applying this method for fault diagnosis, careful selection and adjustment of these parameters through sensitivity analysis is crucial for achieving optimal diagnostic performance. Furthermore, algorithm enhancements such as adaptive resampling techniques, improved proposal distributions, or hybrid filtering approaches can further increase diagnostic accuracy and reliability. These improvements can expand the method's applicability in practical scenarios, including real-time monitoring systems and predictive maintenance applications where computational efficiency and robustness are critical considerations.