GNPENet: A Novel Convolutional Neural Network With Local Structure for Fault Diagnosis

With the development of modern industry, fault diagnosis has become an important research field. Currently, many methods for fault diagnosis have been proposed. As a method designed to overcome the high computational complexity of the convolutional neural network (CNN), PCANet is also used in the field of fault diagnosis. The core algorithm of the PCANet is the principal component analysis (PCA) algorithm. However, PCA is a global dimensionality reduction method, which cannot effectively analyze the local spatial geometry structure of data and may even weaken or destroy the local structure information. Furthermore, algorithms based on L2-norm are very sensitive to noise and outliers. To address the problems, a generalized neighborhood preserving embedding (GNPE) method based on Lp-norm is first proposed, and it is used to learn the convolutional filters of CNN. In this way, a novel CNN with local structure is proposed, called the GNPE Network (GNPENet). We conduct fault diagnosis experiments on five datasets of three types and comprehensively evaluate the proposed GNPENet method. The experimental results show that, compared with some state-of-the-art models, GNPENet has better feature extraction and generalization capabilities.