A Health Indicator Construction Method Based on Unsupervised Parallel Multiscale Neural Networks

The construction of health indicators (HI) is an essential parts of prognostics and health management (PHM). A good health indicator can accurately reflect the mechanical degradation process. Currently, most health indicator construction methods rely on abundant expert knowledge, but expert knowledge may be challenging to obtain. To solve this problem, this paper proposes a HI construction method based on unsupervised parallel multiscale convolutional long short-term memory neural network (PMCLSTM). Firstly, multiscale convolutional neural network (MSCNN) and multiscale long short-term memory neural network (MSLSTM) were used to extract multi-dimensional local and global features in parallel. Under the action of the LSTM neural network, the local jitter caused by CNN can be effectively reduced. Then, the linear regression (LR) model was used to fuse and reduce the acquired features to construct feature vectors. Finally, the relative similarity of the feature vector between the initial sample data and the current sample data is calculated, and the normalization method is used to construct the health indicator. The model is validated on the railway wagon wheel wear dataset and compared quantitatively with some advanced methods using two metrics, monotonicity and trendability. The experimental results show that the constructed HI is improved in both monotonicity and trendability, and the predicted remaining useful life of the wheel is in good agreement with the real remaining useful life, which can effectively identify the mechanical degradation process.