Locality structure preserving based feature selection for prognostics

Feature selection in data-driven modelling is an important research topic for prognostics. The performance of prediction model may vary considerably under different feature subsets. Hence it is important to devise a systematic feature selection method, which offers the guidance for choosing the most representative features for prognostics. Nowadays, feature selection algorithms in the field of prognostics are largely studied to the type of learning: supervised or unsupervised, which leads to poor generalization between different prognostics applications. In this paper, a unified feature selection method, called locality structure preserving based feature selection (LSPFS), is developed to improve the robustness and accuracy of prognostics under both unsupervised and supervised learning conditions. In LSPFS, the local structure of original data is constructed according to the similarity between data points, and the representative features are selected based on their ability to preserve the local structure. Moreover, by designing different local structure via local information and actual degradation information of the data, the introduced method can unify supervised and unsupervised feature selection, and enable their joint study under a general framework. Experiments on NASA turbofan engine simulation dataset and lithium-ion battery dataset are conducted to test and evaluate the proposed algorithm.