On the solidification of the manifold of the t-distributed stochastic neighbour embedding for condition classification of machine tools

Abstract

t-distributed stochastic neighbour embedding (t-SNE) is of considerable interest in machining condition monitoring for feature selection. In this paper, the neural networks are introduced to solidify the manifold of the t-SNE prior to classification. This leads to the improved feature selection method, namely the Net-SNE. Conventional statistical features are first extracted from vibration signals to form a high dimensional feature vector. The redundancies in the feature vector are subsequently removed by the t-SNE. Then the neural networks build a mapping model between the high dimensional feature vector and the selected features. The new data is calculated directly using the mapping model. The experiments were conducted on a lathe and a milling machine to collect vibration signals under common working conditions. The K-nearest neighbour classifier is applied to a small sample case and a class-imbalance case to compare the classification performance with and without the Net-SNE. The results demonstrate that the Net-SNE has the advantage over the t-SNE, since it can mine the discriminative features and solidifiy the manifold in the calculation of the new data. Moreover, the proposed method significantly improves the classification accuracy by Net-SNE, along with better classification performance in data-limited situations.