摘要
针对故障出现时系统的非线性行为及劣化趋势预测问题,基于混沌理论及非线性动力学,提出以最大Lyapunov指数作为评价运行状态的指标,通过Elman-决策树对其进行预测及分类的故障预警方法。并以轴承数据为例对该方法进行了验证,对正常轴承、外环人工电火花加工出裂纹轴承、外环钻孔的轴承壳体振动信号时序数据进行处理,计算降噪后的最大李雅普诺夫指数,并得出了不同故障轴承的最大Lyapunov指数具有明显差异的结论。以其为基础,建立模型逐层学习实现指标预测与故障识别,仿真结果表明,该方法可实现较高的预测准确率。该方法克服了Lyapunov指数对噪声敏感的特性,并且可以通过避免识别多种故障时需训练多个神经网络,因而降低计算量,为故障预警领域一种新途径。
Based on chaos theory and nonlinear dynamics,the fault warning diagnosis method is proposed where the maximum Lyapunov exponent is the evaluation index for the operating state,and the Elman-decision tree is used to predict and classify the failures.The method is testified by using a data set proposed by Paderborn University,which is measured from a normal bearing,an outer ring artificially Electrical Discharge Machined(EDM)bearing and outer ring drilled bearing.The result shows that the maximum Lyapunov exponent can effectively distinguish the data sets from different fault states.Then an Elman-decision tree model is established and trained to realize the maximum Lyapunov exponent prediction and fault identification,which is proved of a relatively high accuracy based on the simulation results.The proposed method can achieve good performance even in presence of moderate noise,and can reduce the computation burden by avoiding training multiple corresponding neural networks for all the faults,and it provides a new idea for fault warning diagnosis.
作者
黄海兵
王卫玉
李崇仕
侯凯
郑阳
陈启卷
HUANG Hai-bing;WANG Wei-yu;LI Chong-shi;HOU Kai;Zheng YANG;CHEN Qi-juan(Wuling Power Corporation Ltd.,Changsha 410004,China;Hydropower Industry Innovation Center of State Power Investment Corporation Ltd.,Changsha 410004,China;School of Power and Mechanical Engineering,Wuhan University,Wuhan 430072,China)
出处
《中国农村水利水电》
北大核心
2022年第2期168-173,共6页
China Rural Water and Hydropower
基金
国家电力投资集团统筹科研项目“水轮发电机组关键部件故障诊断研究”(TC2020SD01)。
