Real-Time Bearing Remaining Useful Life Estimation Based on the Frozen Convolutional and Activated Memory Neural Network

Abstract

Bearings are widely used in rotating machinery, such as aircraft engines and wind turbines. In this paper, we proposed a new data-driven method called frozen convolution and activated memory network (FCAMN) for bearing remaining useful life (RUL) estimation based on the deep neural network. The proposed method is composed of two parts: the multi-scale convolutional neural network is carried out to pre-train the raw data to directly obtain the global and local features, and the second step is accomplished by the convolutional-memory neural network, which enables to connect the convolutional layer with the long-short-time-memory layer together to predict the continuous bearing RUL. Compared with the traditional networks, the proposed network can additionally extract both the global and local information on the vertical feature axis and the associated context information on the horizontal time axis. The experiments are conducted to prove that the proposed method requires fewer training samples and outperforms other methods in RUL estimation.

Keywords

• Bearings
• remaining useful life estimation
• multi-scale convolutional network
• long short time memory neural network