Back propagation algorithm used for tuning parameters of ANN to supervise a compressor in a pharmachimical industry

Abstract

This paper presents the retro-propagation algorithm for tuning the parameter of Artificial Neural Networks used by pharmachemical industry. The obtained numerical test results on lubrication and air circuits shown that the proposal improves the performance in terms of number of iterations and reliability of the models. BEKER Laboratories production line, is a Pharmaceutical production company located at Dar El Beida (Algiers-Algeria), was kept as the main target of this study. After careful inspection, the weakest and the strongest points of the system were identified and the most strategic equipment within the line (the compressor) was taken as the equipment of focus. From this specific point, failure simulations are most adequate and from this selected target, the designed system will be better positioned for failure detection during the production process. The efficiency of this approach is its fast learning, and its accuracy of detecting failure which is of the order of 10-3