An Improved PixelHop Framework and its Application in Rolling Bearing Fault Diagnosis

Abstract

The PixelHop framework based on successive subspace learning (SSL) has been widely used in signal processing and computer vision, which can effectively improve the classification accuracy in high spatial resolution scenes through successive subspace growth. To solve the problems of insufficient feature extraction and dependence on prior knowledge in the PixelHop framework, an improved PixelHop (I-PixelHop) framework is proposed. On the basis of PixelHop framework, I-PixelHop has made the following improvements. 1) I-PixelHop fully extracts the continuous features in one-dimensional sequence data through the improved neighborhood expansion, which can provide a richer feature set. 2) The improved label-assisted regression (ILAG) unit uses the Bi-K-Means clustering algorithm to enable more correct clustering of similar samples, and it adopts the cross-entropy threshold method to alleviate the negative effects caused by the improper setting of the number of pseudo-classes. 3) The high-dimensional features are fully reused by adopting the pseudo dense connection structure to obtain a better feature set. Moreover, the proposed I-PixelHop framework is applied to the rolling bearing fault diagnosis. A series of experiments are carried out to verify the effectiveness of the proposed I-PixelHop. The experimental results show that the fault diagnosis accuracy of I-PixelHop can reach 98.91% and 98.74% on the two different rolling bearing fault datasets, and it also has satisfactory anti-noise ability, faster training speed, and smaller model size.

Keywords

• Fault diagnosis
• machine learning
• object classification
• rolling bearing
• successive subspace learning