摘要
滚动轴承的运行状态对整机工作状态影响重大,但目前其故障诊断方法存在依赖手工特征提取、鲁棒性不高等问题.因此,本文提出了一种基于改进的一维卷积神经网络(1D-CNN)和长短期记忆网络(LSTM)集成的滚动轴承故障诊断方法 (1D-CNN-LSTM).首先,利用改进的1D-CNN-LSTM模型对滚动轴承6种不同的工作状态进行了分类识别实验,实验结果表明提出的分类模型能够以较快的速度识别出滚动轴承的不同状态,平均识别准确率达99.83%;其次,将提出的模型与部分传统算法模型进行对比实验,结果表明所提方法在测试精度方面有较大优势;最后,引入迁移学习测试模型的鲁棒性和泛化能力,实验结果表明提出的改进模型在不同工况下有较好的适应性和高效性,模型有较强的泛化能力,具备工程应用的可行性.
The state of rolling bearings has a great influence on the working state of the whole machine, but the fault diagnosis method of the rolling bearings at present has some problems, such as dependency on manual feature extraction and low robustness. Therefore, we propose a fault diagnosis method of rolling bearings(1 D-CNN-LSTM) based on the improved integration of 1 D Convolutional Neural Network(1 D-CNN) and Long Short-Term Memory(LSTM) network.Firstly, the 1 D-CNN-LSTM model is used to classify and identify six different working states of rolling bearings. The experimental results indicate that the proposed classification model can identify different states of rolling bearings at a high speed, with an average identification accuracy of 99.83%. Secondly, the proposed model is compared with some traditional algorithm models and shows great advantages in measuring accuracy. Finally, transfer learning is introduced to test the robustness and generalization ability of the proposed model. The experimental results demonstrate that the model proposed in this study has good adaptability and high efficiency under different working conditions, featuring strong generalization ability and engineering application feasibility.
作者
曹正志
叶春明
CAO Zheng-Zhi;YE Chun-Ming(Business School,University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《计算机系统应用》
2021年第3期126-133,共8页
Computer Systems & Applications
基金
国家自然科学基金(71840003)
上海理工大学科技发展基金(2018KJFZ043)。
关键词
故障诊断
卷积神经网络
长短期记忆网络
深度学习
迁移学习
fault diagnosis
Convolutional Neural Network(CNN)
Long-Short Term Memory(LSTM)network
deep learning
transfer learning
