An extended Kalman filter and neural network cascade fault diagnosis strategy for the glutamic acid fermentation process

The purpose of this paper is to present results that were obtained in fault diagnosis of glutamic acid fermentation process. The diagnosis algorithm is based on the extended Kalman filter (EKF) and neural network classifier. Inputs of the network are the process I/O data, such as pressure and temperature, parameters estimated by EKF, and state values calculated by dynamic equations, while outputs of the network are process fault situations. A batch glutamic acid fermentation process is studied as a test case, which is with 13 measurements, five estimated parameters, three calculated states, and 11 fault situations. The running test results show that the strategy appears to be better suited to diagnose faults of such an industrial process.