Abstract:

To solve the problems of equipment's failure prediction, including insufficient effective samples and low accuracy of the prediction model, the grey theory and neural network method are integrated in this study, and the combined models are proposed. On the basis of the new information priority principle and the reconstruction background value method, the initial and background values of the GM(1, 1) model are optimized, and the back propagation neural network model is improved by the Levenberg-Marquardt algorithm. By using combined forecasting theorem, the improved grey model and neural network model are integrated with multiple approaches and three combined models are established based on weight allocation, error correction and structural optimization respectively. Selecting the failure prediction of a radar transmitter as an example, the effectiveness of the proposed methods is verified. The results show that compared with the existing prediction model, more accurate failure prediction results can be obtained with the proposed combined models. Therefore, it is helpful for equipment's failure prediction and predictive maintenance.

Key words: failure prediction, grey model, neural network, combined model