中国机械工程2019，Vol.30Issue(2)：196-204,9.DOI:10.3969/j.issn.1004-132X.2019.02.010
基于深度学习多样性特征提取与信息融合的行星齿轮箱故障诊断方法
Planetary Gearbox Fault Diagnosis Based on Multiple Feature Extraction and Information Fusion Combined with Deep Learning
摘要
Abstract
According to the heavy noises of vibration signals and the difficulty of incipient fault diagnosis for planetary gearboxes using single classifier, a method of planetary gearbox fault diagnosis was proposed based on multiple feature extraction and information fusion combined with deep learning.The multiple excellent stacked denoising autoencoders (SDAEs) were acquired based on multi-objective evolutionary algorithm.Then, multi-response linear regression model was employed to integrate multiple SDAEs for building multi-obiective ensemble stacked denoising autoencoders (MO-ESDAEs), which was used to diagnose faults of planetary gearboxes.The experimental results show that the proposed method may enhance the fault diagnosis accuracy and stability.关键词
行星齿轮箱故障诊断/深度神经网络/多样性特征提取/多目标进化算法Key words
planetary gearbox fault diagnosis/deep neural network/multiple feature extraction/multi-objective evolutionary algorithm分类
机械制造引用本文复制引用
金棋,王友仁,王俊..基于深度学习多样性特征提取与信息融合的行星齿轮箱故障诊断方法[J].中国机械工程,2019,30(2):196-204,9.基金项目
国家自然科学基金资助项目(61371041) （61371041）
航空科学基金资助项目(2013ZD52055) （2013ZD52055）
国家商用飞机制造工程技术研究中心创新基金资助项目(SAMC14-JS-15-051) （SAMC14-JS-15-051）
