Engineering System Fault Detection Using Particle Filters Available to Purchase

This paper presents an approach to machinery fault detection using particle filters (PF). The machine vibration signals are processed using morphological signal processing (MSP) to extract a novel entropy based health index (HI) characterizing the signal shape-size complexity. The evolution of HI is approximated as a nonlinear state space model using a computational intelligence (CI) technique. PF is used to estimate the progression of HI in presence of observation and process noise. The PF based approach is illustrated for estimation of state and parameters of a chaotic system. The feasibility of the approach is demonstrated through vibration dataset of a helicopter drive-train system gearbox. The results help understand the relationship of the system condition, the corresponding HI, the level of degradation and its progression in a stochastic environment using Bayesian learning.