Health indicator construction of machinery based on end-to-end trainable convolution recurrent neural networks

In the field of prognostic and health management of engineered systems, health indicator construction of bearings is one of the most significant and challenging problems. Many data-driven approaches centered on deep learning have been proposed recently in the context of smart manufacturing, where massive condition monitoring data could be collected. Among them, there are two representative methods, i.e., the convolution neural networks (CNN) based method and the recurrent neural networks (RNN) based method. However, there are some problems with them. The former has small receptive field size and cannot encode time-series information that is crucial for determining the bearings degradation degree, while the latter need hand-crafted features with prior knowledge of experts. Aimed at these problems, an intelligent and end-to-end health indicator construction approach is proposed. It combines structural advantages of previous two methods. It firstly converts the original input data into a series of local features that maintain chronological order in the convolution feature map. Then the sequential local features are elegantly connected by a recurrent neural network, which makes the extracted features in the recurrent layer contain global semantic information with time series. The bearing experiment under two different operating conditions demonstrates that the proposed method is reliable and effective in establishing bearing health indicator and characterizes the nonlinear degradation trend of bearings into approximately linear process over time. The experimental results also show that the proposed method achieves better results concerning trendability and monotonicity, compared with the CNN-based method and the RNN-based method.