Deep convolution feature learning for health indicator construction of bearings

In the field of data-driven prognostics of bearings, considerable research effort has been taken to construct an effective health indicator. However, existing health indicator construction methods are mainly based on manual feature extraction and feature fusion techniques. Such manual techniques are generally designed for specific tasks and need the help of experts' prior knowledge, resulting in labor-consuming and time-costing. So it is desirable to automatically construct health indicators. To deal with this problem, this paper presents a deep convolution feature learning based method to construct health indicators of bearings. The proposed method first learns features from the raw vibration signals through several convolution and pooling operations. Then the learned features are mapped to the health indicator through a nonlinear transformation. At last, the proposed method is validated by a bearing dataset. The results demonstrate that the proposed method is able to effectively construct the health indicator directly from the raw vibration signals, which is superior to that based on self organizing map. Additionally, because the proposed health indicator is constructed automatically, it significantly reduces the need of experts' prior knowledge and labor resources.