Track: Quality Control and Quality Management

Abstract

The increasing complexity of most industrial processes always tends to create problems in monitoring and supervision systems. Detection and early fault diagnosis are the best way to manage and solve these problems. Artificial neural networks (ANNs), by their ability to learn and store a large volume of information, are tools particularly suitable for diagnostic support systems. Effectiveness of ANNs for fault diagnosis in  milk pasteurization process is presented in this paper. The initial data base used for fault diagnosis is constructed using data extracted from FMEA (Failure Modes and Effects Analysis) tables of milk pasteurization process. Indeed, this analysis makes it possible to establish the links of cause and effect between the faulty components and the observed symptoms. Three models of ANNs, namely Feed-Forward Back Propagation (FFBP), Radial Basis Function based Neural Network (RBNN), and Generalized Regression Neural Networks (GRNN) are developed and compared. The determination coefficient (R²), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) statistics were used as evaluation criteria of all the models. The comparison results indicate that the performances of GRNN model are better than the FFBP and RBNN models. The same neuronal models can be extended to any technical system by considering appropriate parameters and defects.