Evolutionary neural network modeling for forecasting the field failure data of repairable systems
An accurate product reliability prediction model can not only learn and track the products reliability and operational performance, but also offer useful information for managers to take follow-up actions to improve the product quality and cost. This study proposes a new method for predicting the reliability for repairable systems. The novel method constructs a predictive model by employing evolutionary neural network modeling approach. Genetic algorithms are used to globally optimize the number of neurons in the hidden layer and learning parameters of the neural network architecture. Moreover, two case studies are presented to illustrate the proposed method. The prediction accuracy of the novel method is compared with that of other methods to illustrate the feasibility and effectiveness of the proposed method.
neural network model repairable system reliability prediction genetic algorithms