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.
2019,41(11): 71-76 收稿日期：2018-09-14
DOI：10.3404/j.issn.1672-7649.2019.11.014
分类号：TH17
基金项目：国防科技工业局MPRD计划资助项目
作者简介：刘胜兰(1989-),女,工程师,主要从事动力传动系统故障仿真、信号处理研究等
参考文献：
[1] 董绍江. 基于优化支持向量机的空间滚动轴承寿命预测方法研究[D]. 重庆:重庆大学, 2012.
[2] 周晓君. 基于样本熵理论的进化论自适应消噪法[J]. 舰船科学技术, 2014, 36(5):55-59
[3] ZHANG B, ZHANG L, XU J. Degradation Feature Selection for Remaining Useful Life Prediction of Rolling Element Bearings[J]. Quality and Reliability Engineering International, 2016, 32(2):547-554
[4] N. PATRICK, G. RAFAEL, M. KAMAL, et al. PRONOSTIA:An Experimental Platform for Bearings Accelerated Life Test[C]. In Proceedings of IEEE International Conference on Prognostics and Health Management, 2012, 1–8.
[5] TIPPING M E. The Relevance Vector Machine[C]//Advances in Neural Information Processing Systems. DBLP, 2000.
[6] 张志华, 梁胜杰, 等. 基于SVM的双层圆柱壳体机械动噪声数据特征提取方法比较[J]. 船舶力学, 2015(Z1)
[7] 常飞, 乔欣, 张申, 等. 基于MFCC特征提取的故障测评与评价方法[J]. 计算机应用研究, 2015(06)
[8] 龚晨. 曲线的随机拟合及其自适应算法[J]. 上海交通大学学报, 2005(4)
