High efficiency fault-detection and fault-tolerant control approach in Tennessee Eastman process via fuzzy-based neural network representation

Abstract

We looked at the background of fault-detection and fault-tolerant control algorithms to propose a new high efficiency one with a focus on Tennessee Eastman process through fuzzy-based neural network representation. Due to the fact that the openloop system may not be stabilized, an advanced control strategy to generate proper control signals needs to be designed. At first, to detect and identify the fault, data preprocessing theories have been considered. Based upon the matter disclosed, to provide a reliable decision-maker block, fusion classifier idea has been realized. For this one, raw data, time, and frequency characteristics are divided into various classification tools and finally the obtained knowledge combination regarding each one of them is adopted. It should be noted that the proposed implementation tools are taken into real consideration as the fuzzy-based neural network representation. Subsequently, the fault-tolerant control approach based on local controller regulation in case of each fault occurrence has been researched, which the investigated outcomes emphasize the effectiveness of the approach proposed here.

Enhancing the accuracy and precision of forecasting the productivity of a factory: a fuzzified feedforward neural network approach

“…In the proposed FFNN approach, an FFNN [1,3,27] is constructed to forecast the productivity of a factory. The training of the FFNN is divided into three steps.…”