Wear prediction of high performance rolling bearing based on 1D-CNN-LSTM hybrid neural network under deep learning

Abstract

The finished precision rolling bearings after processing are required to pass the life test before they can be put into the market. The life testing takes a lot of time and expense. Aiming to solve the problem of time and expense, the 1D-CNN and 1D-CNN-LSTM hybrid neural networks are used for deep learning based on the existing rolling bearing life big data results (a total of 791152 date). Taking the wear of bearing as the target, the life prediction of bearing is carried out by using Python. The results show that: (1) 1D-CNN-LSTM algorithm and ''all parameters” are selected as the best prediction options. (2) ''XYZ direction displacement” and ''all parameters” have the best fitting effect on the predicted wear value, and the MAPE is 4.18877, 1.2102, 2.68903 and 1.19981, respectively. The 1D-CNN-LSTM algorithm is slightly better than the 1D-CNN algorithm. (3) Using 1D-CNN-LSTM algorithm and ''all parameters” to predict the bearing wear life will obtain good results. Compared with the highest 1D-CNN and ''Four Bearing Temperatures” parameters, it is reduced by 14.7 times. (4) The prediction process and results provide a wear prediction method for relevant bearing enterprises in the experimental running-in stage. It can also provide reliable research ideas for subsequent related enterprises and scholars.

Keywords

• Rolling bearing
• 1D-CNN-LSTM hybrid neural network
• Mixed prediction of wear
• Deep learning
• Big data