Modeling and predicting remanufacturing time of equipment using deep belief networks

Predicting optimal remanufacturing time is a key point for improving the quality performance and economic effectiveness of multi-life cycle of remanufactured equipment, and knowing the manufacturing cost is a critical point for accurate prediction of optimal remanufacturing time. On the basis of understanding equipment multi-life cycle and cost composition, this paper investigated the correlation between optimal remanufacturing time and remanufacturing cost. Due to the randomness and nondeterminacy of remanufacturing cost, the limitation of available data sample, the insufficiency of shallow learning algorithm, we proposed deep belief networks (DBN) prediction model to improve the prediction accuracy of optimal remanufacturing time. In such model, the planning cost, disposal service cost, remanent value, annual cost increment, parts remanufacturing ratio, parts replacement ratio and technical indexes were adopted as inputs, while optimal remanufacturing time was regarded as output. The proposed algorithm can automatically extract more abstract and more expressive characteristics from sample, thus realizing complex nonlinear mapping between input and output data. In this research, DBN algorithm was used to estimate the training samples and test samples of mechanical transmission, and the predicting error of DBN algorithm was only 27% of the BP training network. The results of our prediction experiments verified the feasibility and effectiveness of the proposed model algorithm method.