A singular pencil model fault diagnosis strategy for an industrial process

In this paper, a singular pencil (SP) model-based fault diagnosis strategy is developed from the viewpoint of practical control application. The diagnosis scheme is based on a SP model of a lumber drying process, which reveals nonlinear and time-variant behavior due to unmeasurable and measurable disturbance. The fault or failure in the drying process is difficult to detect and diagnosis due to complex dynamic nonlinearities, coupling effects among key variables, and process disturbances caused by the variation of lumber sizes, species, and environmental factors. Thus, on-line information of the system state and parameter variation is required for fault diagnosis, while the SP model can result in a simultaneous on-line joint state and parameter estimator based on the ordinary Kalman filter. As the parameters are estimated together with the state in real-time, an on-line fault diagnosis scheme can be designed by using these estimated parameters and states. A wood-drying kiln is studied as a test case, which is with two actuators and four outputs, 8 estimated parameters and states, and 11 fault situations. The simulation results show that the strategy appears to be better suited to diagnose faults of such an industrial process.