Abstract:

Due to the multi-type and multi-dimension characteristics of the industrial big data, the single deep learning network can not extract the performance degradation characteristics effectively from the data. To solve the problem, the health indicator construction model based on the multi-input hybrid deep learning network is proposed, which can fuse and process the one-dimensional time series data and two-dimensional pictorial data simultaneously. According to the characteristic of the input data, the constructed hybrid deep learning network mainly is consisted of the time features extraction layer, spatial features extraction layer, fusion layer and fully connected layer. The time features extraction layer is constructed by stacking the multiple long short-term memory (LSTM) network to extract temporal features from the one-dimensional time series data. The spatial features extraction layer is constructed by deep convolutional neural network (DCNN) which is used to extract the spatial features from the two-dimensional pictorial data. The fusion layer is designed to fuse the time features and the spatial features, and the fully connected layer output the health indicator which could reflect the performance degradation process. The rolling element bearing life-cycle experiment indicates that the health indicator construction method based on the multi-input hybrid deep learning network could extracts and fuses different data type and reduces the discreteness of the degradation curve, which reduces the uncertainty of the remaining useful life prediction results. What is more, the proposed method could promote the monotonicity and trendability. The remaining useful life prediction result shows that the proposed health indicator could promote the remaining useful life prediction accuracy effectively.

Key words: industrial big data, deep learning, health indicator, evaluation criterion, discreteness