摘要
轴承智能故障诊断是机械大数据状态监测的热门研究领域,传统的数据驱动故障诊断方法对基于信号处理的特征提取环节极为耗时,并且对专家经验要求高,为消除其带来的参数预定义影响,实现快速特征提取的同时提高识别率,在研究一维卷积神经网络故障诊断方法的基础上,提出一种用于轴承故障诊断的二维卷积神经网络优化方法。该方法引入了一种新的数据预处理方式,将原始时域信号数据转换成二维灰度图像来提取转换后的图像特征,消除手工特征的影响;同时,在验证分类前对实验采集故障数据集添加了降噪处理,并对卷积神经网络梯度下降算法进行参数自适应学习率优化。仿真与实验结果表明,所提出的二维优化卷积神经网络故障诊断方法在选取64×64的信号–图片转换格式下,AMSGrad算法能将故障预测模型的准确度提升至98%,训练速度更快,同时具有更高的分类准确性和抗噪性能,使其在实际转速范围内能达到损失小于5%的识别准确率。
Intelligent bearing fault diagnosis is a hot research field of mechanical big data condition monitoring. The traditional data-driven fault diagnosis method is extremely time-consuming and requires high expert experience for signal extraction-based feature extraction. In order to eliminate the pre-defined effects of parameters and improve the recognition rate while improving the feature extraction, this paper proposed a two-dimensional convolutional neural network optimization method for bearing fault diagnosis based on the research of one-dimensional convolution neural network fault diagnosis method. This method introduced a new data preprocessing method, which converted the original time domain signal data into a two-dimensional gray image to extract the transformed image features, eliminated the influence of manual features,and collects the experimental errors before verifying the classification. The data set adds noise reduction processing and optimizes the parameter adaptive learning rate for the convolutional neural network gradient descent algorithm. The simulation and experimental results show that the proposed two-dimensional optimized convolutional neural network fault diagnosis method is based on the signal-picture conversion format under 64×64. The AMSGrad algorithm can improve the accuracy of the fault prediction model to 98%, and the training speed is faster and higher. Classification accuracy and anti-noise performance can achieve a recognition accuracy of less than 5% loss in the actual speed range.
作者
肖雄
王健翔
张勇军
郭强
宗胜悦
XIAO Xiong;WANG Jianxiang;ZHANG Yongjun;GUO Qiang;ZONG Shengyue(Institute of Engineering Technology, University of Science and Technology Beijing, Haidian District, Beijing 100083, China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2019年第15期4558-4567,共10页
Proceedings of the CSEE
基金
国家自然科学基金项目(61873025)
中国博士后科学基金面上资助项目(2018M631340)
教育部中央高校基础科研费项目(FRF-TP-17-045A1)~~
关键词
深度学习
故障诊断
信号转换
卷积神经网络
deep learning
fault diagnosis
signal conversion
convolutional neural network
