Assessing the failure frequency of potential hazardous incidents using radial basis function networks (RBFN). A milk pasteurization unit as study case

A case study involving analyses of the reliability was made from a selected pasteurization plant. The study involved a probability of failure and mitigation of selected typical installation. Recently, new studies indicate that the RBFN is a good tool for prediction of parameters such that the aim of this paper is to apply a RBFN approach as predictive model focus on the context of reliability. The proposed method herein is based on clustering of input space vectors and computing weights of Euclidian distances and histogram equalization within each cluster will determine the centre and width of each receptive field. The results of this paper indicate that the accuracy of the prediction obtained can be around 99.7%. Also, the model indicates that there is 9.2% additional risk in extending the operation from fourth to fifth year for major components having survived 3 years.