Abstract
Fault Detection and Diagnosis (FDD) is important in many industries to provide safe operation of a process. Actuator faults, sensor faults and process faults are the common faults occurring in chemical process. In this present work Sensor and Process faults of Spiral type heat exchanger is detected and diagnosed using ANN. NARX network (Nonlinear Autoregressive with External input) is used as ANN network structure. Network is trained using Levenberg, Bayesian and Scaled Conjugate Gradient Methods. To achieve FDD, a set of residuals is generated by ANN which indicates the state of the system and provide information about faults. Mean Square Error, Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE) and Integral Square Error (ISE) is obtained for the above said methods which are shown in simulation results.
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