A Multi-agent Based Failure Prediction Method Using Neural Network Algorithm

A continuous monitoring system with high reliability is significantly important for complex equipment which is usually expensive, large-scale and sophisticated. Once a failure happens, it brings about not only serious economic losses, but also potential security hazards. In order to overcome outage damage caused by temporary failure and ensure excellent operation of the equipment, this paper presented an effective prediction model which combined the back propagation neural network (BPNN) with multi-agent cooperation grouping algorithm. The values of weights and thresholds of BPNN were obtained through optimization results of the multi-agent cooperation grouping algorithm. Based on above initialization parameters which met corresponding demands, repeated BPNN training was utilized to forecast fault. Case study on continuous casting equipment validated that the proposed model is valid for failure prognosis with forecasting accuracy elevated, compared with classical BPNN prediction method. Another comparison, function approximation experiment on the basis of a benchmark function, also showed that the suggested method is superior to BPNN in convergence speed.