Detection of Single and Dual Incipient Process Faults Using an Improved Artificial Neural Network

Changes in the physicochemical conditions ofpro cess unit, even under control, may lead to what are generically referred to as faults. the cognition ofcauses is very important, because the system can be diagnosed andfault tolerated. in this article, we discuss andpropose an artificial neural network that can detect the incipient and gradual faults either individually or mutually. the main feature ofthe proposed network is including the fault patterns in the input space. the schemeis examined through a sample unit with five probable occurring faults. the simulation results indicate that the proposed algorithm can detect both single and two simultaneous faults properly.