Fault detection of rotating machinery using model-based techniques

This work formulates the problem of incipient fault detection and diagnostics for rotating machinery in a statistical model-based framework. This includes problem description, modeling of rotating machinery and fault mechanisms, formulation of the detection and diagnostics problem, and an implementation of the proposed scheme in a simulation environment to test the feasibility of this approach. More specifically, a multiple model nonlinear filtering algorithm is proposed for fault detection and diagnostics in a statistical framework. A simulation study, which includes normal and different fault modes, illustrates the potential of the proposed approach, especially in the presence of measurement noise and process uncertainty.