摘要
针对当前齿轮箱故障诊断需要进行复杂的特征提取以及识别准确率不高等问题,提出了一种基于堆栈稀疏自编码器(SSAE)的齿轮箱故障诊断方法,采用时域分析对故障信号进行特征预处理,然后将其输入稀疏自编码器网络中进行特征优化以及降维,提取出表征信号本质信息的特征,最后将其输入到Softmax分类器中实现齿轮箱故障的分类。实验结果表明,该方法在相同工况和混合工况下的均能达到较高的识别精度,在混合工况下,其识别精度达到99.5%,高于文中提出的其他模型,因此该方法能有效地用于齿轮箱故障诊断。
Aiming at the complex feature extraction and low recognition accuracy of gear box fault diagnosis,a fault diagnosis method based on stack sparse self-encoder( SSAE) is proposed. The fault signal is pre-processed by time domain analysis,and then it was input into sparse self-encoder network for feature optimization and dimensionality reduction to extract tables. The feature of intrinsic information of signature signal is input into Softmax classifier to classify surface defects of strip. The experimental results show that the proposed method can achieve high recognition accuracy under the same and mixed conditions. For the mixed conditions,the recognition accuracy reaches 99.5%,which is higher than other models proposed in this paper. Therefore,the proposed method can be effectively applied to gear box fault diagnosis.
作者
任君兰
曹浩
司吉兵
REN Junlan;CAO Hao;SI Jibing(School of Mechanotronics&Vehicle Engineering,Chongqing Jiaotong University,Chongqing 400074,CHN;Changqing Key Laboratory of System Integration and Control for Urban Rail Transit Vehicle,Chongqing 400074,CHN)
出处
《制造技术与机床》
北大核心
2020年第5期49-53,共5页
Manufacturing Technology & Machine Tool
基金
国家自然基金项目(51775072)
重庆市基础与前沿研究计划项目(cstc2016jcyjA0526)
重庆市教委科学技术研究项目(KJ1600519)。
关键词
齿轮箱故障
稀疏自编码器
特征优化
故障诊断
gear box fault
sparse auto-encoder
feature optimization
fault diagnosis
