摘要
针对传统滚动轴承故障诊断方法在大噪声与变载荷环境下诊断困难的问题。基于混沌理论,通过卷积神经网络(CNN)与双向长短期记忆网络(BiLSTM)提出CCNN(Chaotic CNN)-BiLSTM智能故障诊断方法。采用相空间重构法将一维时间序列转化为二维混沌序列,学习并提取混沌序列中有效非线性信息,并输入Softmax层中完成分类。结果表明,较之现有方法,所提CCNN-BiLSTM方法在变载荷和大噪声(信噪比为-8 dB)环境下的准确率分别至少高出3.76%与5.21%,表明该方法具有良好的鲁棒性和泛化性能。
Here,aiming at the problem of traditional rolling bearing fault diagnosis methods being difficult to diagnose faults under environments of large noise and variable load,based on chaos theory,combing convolution neural network(CNN)and bidirectional long-short term memory network(BiLSTMN),the chaotic CNN-BiLSTMN(CCNN-BiLSTMN)intelligent fault diagnosis method was proposed.The phase space reconstruction method was used to transform one-dimensional time series into two-dimensional chaotic sequences,effective nonlinear information in the chaotic sequences was learned,extracted,and input into layers of the software Softmax to complete classification.The results showed that compared with the existing methods,the accuracy of the proposed CCNN-BILSTMN method can at least increase by 3.76%and 5.21%under environments of variable load and large noise with signal-to-noise ratio of-8 dB,respectively;CCNN-BILSTMN method has good robustness and generalization performance.
作者
金江涛
许子非
李春
缪维跑
孙康
肖俊青
JIN Jiangtao;XU Zifei;LI Chun;MIAO Weipao;SUN Kang;XIAO Junqing(College of Energy and Power Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;Shanghai Municipal Key Lab of Multiphase Flow and Heat Transfer in Power Engineering,Shanghai 200093,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2022年第17期160-169,共10页
Journal of Vibration and Shock
基金
国家自然科学基金(51976131,52006148)
上海市“科技创新心动计划”地方院校能力建设项目(19060502200)。
关键词
卷积神经网络
双向长短期记忆网络
混沌理论
轴承
故障诊断
convolutional neural network(CNN)
bi-directional long-short term memory network(BiLSTMN)
chaos theory
bearing
fault diagnosis