所属单位：

机电工程学院

发表刊物：

IEEE ACCESS

关键字：

Feature extraction；Fault diagnosis；Vibrations；Machinery；Data models；Generative adversarial networks；Graph neural networks；Rotating machines；Attentional mechanism；fault diagnosis；gram angle difference field；generative adversarial network；graph neural network (GCN)；rotating machinery

摘要：

In practical engineering scenarios, machines are seldom in a faulty operating state, so it is difficult to get enough available sample data to train the fault diagnosis model, leading to the problem of the small and unbalanced number of rotating machinery fault samples and low fault diagnosis accuracy. To solve this problem, this paper introduces a novel approach to machinery fault diagnosis. This approach involves the integration of a Convolutional Attention Residual Network (CBAM-ResNet) with a Graph Convolutional Neural Network (GCN). Firstly, to comprehensively exploit time-domain information from one-dimensional vibration signals, this study utilize Gram Angular Field (GAF) coding to transform traits of vibration signals into two-dimensional image characteristics. The resultant two-dimensional image is then expanded by applying the Wasserstein Distance Gradient Penalty Generation Adversarial Network (WGAN-GP) to produce a representative sample image. Secondly, the image is input to CBAM-ResNet to perform focused feature extraction and construct the feature matrix. Lastly, the adjacency matrix is derived through Graph Generation Layer (GGL); subsequently, the feature matrix and adjacency matrix are utilized as inputs for the GCN. After deep feature extraction, fault feature classification is executed via Softmax. Performance tests were conducted using the Case Western Reserve University bearing dataset and the planetary gearbox dataset. The method demonstrated remarkable results, achieving an accuracy of over 99% on the unbalanced dataset and surpassing 98% in 0dB noise compared to various other models. This illustrates the effectiveness and feasibility of the proposed method.

第一作者：

史丽晨,王海涛

论文类型：

期刊论文

通讯作者：

代曦阳,李明俊,刘泽林,王瑞华

卷号：

12/34785-34799

ISSN号：

2169-3536

是否译文：

否

CN号：

2169-3536

发表时间：

2024-02-21