Deep separable convolutional network for remaining useful life prediction of machinery

Abstract

Abstract Deep learning is gaining attention in data-driven remaining useful life (RUL) prediction of machinery because of its powerful representation learning ability. With the help of deep learning techniques, the machine degradation information can be mined more sufficiently and some promising RUL prediction results have been achieved in several studies recently. These deep learning-based prognostics approaches, however, have the following weaknesses: 1) Their prediction performance largely depends on the hand-crafted feature design. 2) The correlations of different sensor data are not explicitly considered in representation learning. To overcome the above weaknesses, a new deep prognostics network named deep separable convolutional network (DSCN) is proposed in this paper for RUL prediction of machinery. In the proposed DSCN, the monitoring data acquired by different sensors are directly used as the inputs of the prognostics network. Then, a separable convolutional building block with a residual connection is built based on separable convolutions and squeeze and excitation operations. Through stacking multiple separable convolutional building blocks, the high-level representations are automatically learned from the input data. Finally, the RUL is estimated by feeding the learned representations into the fully-connected output layer. The proposed DSCN is validated using the vibration data from accelerated degradation tests of rolling element bearings and the public degradation simulation data of turbine engines, respectively. The experimental results show that the proposed DSCN is able to provide accurate RUL prediction results based on the raw multi-sensor data and is superior to some existing data-driven prognostics approaches.