Fault Diagnosis of Rotating Machinery Based on Convolutional Neural Network and Singular Value Decomposition

Dong Liu,Pei Zhang,Zhihuai Xiao,Xiao Hu,Xu Lai

doi:10.1155/2020/6542913

Abstract

Vibration signal and shaft orbit are important features that reflect the operating state of rotating machinery. Fault diagnosis and feature extraction are critical to ensure the safety and reliable operation of rotating machinery. A novel method of fault diagnosis based on convolutional neural network (CNN), discrete wavelet transform (DWT), and singular value decomposition (SVD) is proposed in this paper. CNN is used to extract features of shaft orbit images, DWT is used to transform the denoised swing signal of rotating machinery, and the wavelet decomposition coefficients of each branch of the signal are obtained by the transformation. The SVD input matrix is formed after single branch reconstruction of the different branch coefficients, and the singular value is extracted to obtain the feature vector. The features extracted from both methods are combined and then classified by support vector machines (SVMs). The comparison results show that this hybrid method has a higher recognition rate than other methods.

Highlights

With the increasing complexity of mechanical structures, it is increasingly important to monitor and diagnose the condition of rotating machinery such as hydropower units and wind turbine system
The vibration signal and shaft orbit are important features that reflect the state of mechanical equipment. erefore, an efficient feature extraction and fault diagnosis method plays a significant role in the operation management and condition monitoring of mechanical equipment
In order to verify the effectiveness of the method, the rotor test bed was used as the experimental object for feature extraction. e rotor test bed used in this work is shown in Figure 4. e test bed was driven by a DC motor, and the DH5600 speed controller was used to control its speed. e rotor had a diameter of 10 mm and a length of 850 mm

Summary

Introduction

With the increasing complexity of mechanical structures, it is increasingly important to monitor and diagnose the condition of rotating machinery such as hydropower units and wind turbine system. Erefore, an efficient feature extraction and fault diagnosis method plays a significant role in the operation management and condition monitoring of mechanical equipment. In order to obtain useful information from the vibration signal that can reflect the operating status of mechanical equipment, various signal processing methods have been proposed, such as Fourier analysis [1, 2], empirical mode decomposition (EMD) [3, 4], and wavelet transform [5, 6]. For signals with significant local characteristics, Fourier analysis methods are often powerless. These problems are mitigated by subsequent improvements or new methods, they still exist. Among many signal processing methods, wavelet transform is widely used in fault diagnosis of rotating machinery because of its good time-frequency localization. Lu et al [5] realized the vibration signal of hydropower generating units denoised with multiple wavelets

Methods

Results

Conclusion