Prediction of Machine Deterioration Using Vibration Based Fault Trends and Recurrent Neural Networks

Abstract

High market competition for sales requires companies to reduce the cost of production if they are to maintain their market shares. Since the cost of maintenance contributes a substantial portion of the production cost, companies must budget maintenance effectively. Machine deterioration prognosis can decrease the cost of maintenance by minimizing the loss of production due to machine breakdown and avoiding the overstocking of spare parts. A new prognostic method is described in this paper which has been developed to forecast the rate of machine deterioration using recurrent neural networks. From tests applying the method to the prediction of nonlinear sunspot activities and vibration based fault trends of several industrial machines, the results have shown that the method is promising. It not only evaluates the seriousness of damage caused by faults, but also forecasts the remaining life span of defective components.