Softmax函数
方位(导航)
计算机科学
人工智能
残余物
模式识别(心理学)
断层(地质)
降噪
噪音(视频)
特征提取
支持向量机
灰度
奇异值分解
算法
人工神经网络
像素
地震学
图像(数学)
地质学
作者
Zhigang Feng,Shouqi Wang,Mingyue Yu
标识
DOI:10.1016/j.dsp.2023.104106
摘要
Aiming at the problem that weak faults in rolling bearings make effective fault diagnosis difficult under strong noise, this paper proposes a multilevel denoising technology based on improved singular value decomposition (ISVD) and intrinsic timescale decomposition (ITD), combined with an improved deep residual network (ResNet), for fault diagnosis in rolling bearings. Firstly, the difference ratio (DR) index is introduced to optimize singular value decomposition, combined with ITD for multilevel denoising of strong noise signals. Effective fault information in bearing vibration signals is extracted and converted into grayscale images. Secondly, the multi-scale feature extraction module (MFE-Module) is introduced to enhance the feature extraction capability of ResNet, and the support vector machine (SVM) is used instead of the Softmax function to identify and classify the fault features. The experimental results indicate that, compared with other methods, the proposed method can more accurately realize the fault diagnosis of rolling bearings in strong noise environments.
科研通智能强力驱动
Strongly Powered by AbleSci AI