计算机工程与应用2024，Vol.60Issue(13)：338-344,7.DOI:10.3778/j.issn.1002-8331.2304-0143
改进时序灰度图和深度学习的齿轮箱故障诊断
Gearbox Fault Diagnosis Based on Improved Time Series Gray Scale Image and Deep Learning
摘要
Abstract
A gear box fault diagnosis method based on improved time series gray image and deep learning is put forward in order to solve the problems such as complex actual working environment of gear box,inadequate performance of extracting features by traditional methods and gray image extraction features.After the vibration signal is decomposed into several intrinsic mode components(IMFs)by EEMD,the IMFs are divided into high-frequency and low-frequency components by cumulative mean criterion,in which the high-frequency components are denoised by wavelet threshold.The high frequency IMFs and low frequency IMFs after noise reduction are reconstructed,and the reconstructed signal is coded by gray image method.Two-dimensional improved time series gray image is sent to convolution neural network for training,so as to exert the advantages of convolution network in feature extraction of pictures and display the results by confusion matrix.Finally,the model results and different gray scale images are compared with traditional diagnosis methods.The results show that compared with common gray image and global denoising gray image,the method proposed in this paper improves the accuracy of gear box fault diagnosis by 4 and 1.8 percentage points respectively,and the convergence speed is significantly faster.Compared with BP neural network and ELM diagnosis method,the method proposed in this paper significantly improves the accuracy of gear box fault diagnosis.关键词
集合经验模态分解/故障诊断/改进时序灰度图/深度学习Key words
ensemble empirical mode decomposition/fault diagnosis/improved time series gray scale image/deep learning分类
信息技术与安全科学引用本文复制引用
谢锋云,李刚,王玲岚,刘慧,汪淦..改进时序灰度图和深度学习的齿轮箱故障诊断[J].计算机工程与应用,2024,60(13):338-344,7.基金项目
国家自然科学基金(52265068) （52265068）
江西省研究生创新专项资金项目(YC 2022-s481). （YC 2022-s481）
