Fault Diagnosis Based on Space Mapping and Deformable Convolution Networks

Yunji Zhao,Xiaozhuo Xu,Haibo Zhang,Nannan Zhang,Menglin Zhou

doi:10.1109/access.2020.3040448

Abstract

The data-driven method based on deep learning is one of the popular issues in the field of fault diagnosis. The completeness and representativeness of the feature matrix from massive and high-dimensional fault data have a great impact on fault diagnosis performance. In addition, the ability of deep networks to extract the spatial characteristics between fault data is especially important for the accuracy of fault diagnosis. Therefore, we propose a method based on space mapping and deformable convolution networks (DCN) to ensure diagnostic accuracy by improving the spatial resolution and spatial constraint characteristics, and both the size and shape of the convolution kernel, one of the key steps in DCN, are adjusted adaptively according to the input of different sizes. Original data are projected into a more discriminative space by the combination of CN and PCA (i.e., space mapping). Then, DCN extract spatial constraints between fault data by training. The Case Western Reserve University (CWRU) bearing dataset and Xi’an Jiaotong University and Changxing Sumyoung Technology Co., Ltd. (XJTU-SY) datasets are used as benchmarks to perform experiments. The results demonstrate that the fault diagnosis method proposed in this paper performs well and can achieve 100% accuracy in the first several epochs. Comparative experiments based on 3 deep learning methods that combine preprocessed and unprocessed data with a convolutional neural network (CNN), residual networks (ResNets) and DCN are carried out to further show the advantages of the fault diagnosis method based on space mapping and DCN.

Highlights

R ECENTLY, with the rapid development of computer science and technology, artificial intelligence has played an essential role in industrial manufacturing
After preprocessing by color names (CN) and principal component analysis (PCA)(i.e., space mapping) methods, the data are converted into images to input the
In this paper, a rolling bearing fault diagnosis method based on space mapping and deformable convolution networks is proposed to effectively address the problems of computational perplexity and modeling complexity caused by massive and high-dimensional fault data

Summary

Introduction

R ECENTLY, with the rapid development of computer science and technology, artificial intelligence has played an essential role in industrial manufacturing. Due to the large scale and complexity of industrial control equipment, as the industrial manufacturing process becomes more automated, failures and faults are more likely to occur. Equipment maintenance becomes more important because the occurrence of small faults can cause damage to equipment, which has a serious impact on both the economy and production process [2]. Fault detection and abnormal diagnosis of dynamic systems have attracted much attention in industry and academia [3]. It is necessary to detect and diagnose faults early to reduce abnormal circumstances and unforeseen situations for complex dynamic systems, especially for rolling bearing elements

Objectives

Methods

Conclusion