摘要
为了提高舰船动力传动系统中滚动轴承的故障特征分析及寿命预测能力,需要对选择合适的振动信号特征表征滚动轴承状态的问题进行分析,通过时域,频域,时频域,信息熵等多方面提取滚动轴承的振动特征,构造特征库,综合全面描述滚动轴承的状态信息。提出了一种自适应特征提取方法,通过添加白噪声特征以及融合特征,并使用相关性,单调性,鲁棒性3个指标来综合评价特征,可以自动确定特征维数并筛选出敏感特征子集,并通过实验数据验证了所提方法的有效性。
In order to improve the fault characteristics analysis and prediction ability of the rolling bearing in the ship power transmission system,it is necessary to analyze the problem of selecting the appropriate vibration signal to characterize the state of the rolling bearing,the vibration characteristics of the rolling bearing are extracted from the aspects of time domain,frequency domain,time-frequency domain and information entropy,and the feature library is constructed to comprehensively describe the state information of the rolling bearing.An adaptive feature selection method is proposed.By adding white noise features and fusion features,and using correlation,monotonicity and robustness to comprehensively evaluate features,it is possible to automatically determine feature dimensions and screen out sensitive features.Set and verify the effectiveness of the proposed method through experimental data.
作者
刘胜兰
高凌寒
杜剑维
刘晨
LIU Sheng-lan;GAO Ling-han;DU Jian-wei;LIU Chen(China Ship Research and Development Academy,Beijing 100101,China;Chongqing University,Chongqing 400004,China)
出处
《舰船科学技术》
北大核心
2019年第21期71-76,共6页
Ship Science and Technology
基金
国防科技工业局MPRD计划资助项目。
关键词
滚动轴承
特征提取
残余寿命预测
相关向量机
rolling bearing
feature extraction
residual life prediction
correlation vector machine
