某型发动机滚动轴承故障特征增强方法  

A Fault Feature Enhancement Method for Rolling Bearings of An Engine

作　　者：沈剑 朱恋蝶 李娜[2] 谢志斌 Shen Jian;Zhu Liandie;Li Na;Xie Zhibin(Equipment Project Management Center,Beijing 100071,China;Beijing Electro-Mechanical Engineering Institute,Beijing 100083,China)

机构地区：[1]某装备项目管理中心,北京100071 [2]北京机电工程研究所,北京100083

出　　处：《质量与可靠性》2023年第5期21-28,共8页Quality and Reliability

摘　　要：针对小型发动机滚动轴承退化过程中早期故障特征微弱、容易被强背景噪声掩盖的问题。提出了基于鲸鱼优化算法(Whale Optimization Algorithm, WOA)和最优最小熵解卷积(Multipoint Optimal Minimum Entropy Deconvolution Adjusted, MOMEDA)相融合的故障特征增强算法,并引入了排列熵(Permutation Entropy, PE)作为WOA优化算法中的目标函数。首先,利用MOMEDA解耦采集信号,抑制信号全频带随机噪声和低频噪声,突出故障冲击周期。随后,利用WOA算法优化MOMEDA中的关键参数,引入PE作为评估指标寻找最优解,得到的最优解即为故障冲击特征增强的解耦信号,再通过频域分析提取故障特征频率。试验结果表明,该方法能够有效增强早期微弱故障特征,具有计算结果准确、计算速度快等优势。Aiming at the weak early fault characteristics and easy to be covered by strong background noise in the degradation process of small engines rolling bearings,this paper proposes a fault feature enhancement algorithm based on the fusion of Whale Optimization Algorithm(WOA)and Multipoint Optimal Minimum Entropy Deconvolution Adjusted(MOMEDA),and introduces Permutation Entropy(PE)as the objective function of WOA optimization algorithm.First of all,the signal is decoupled by MOMEDA to suppress the random noise of the full frequency band and the low frequency noise of the signal and highlight the fault shock period.Then,WOA algorithm is used to optimize the key parameters in MOMEDA.PE is used as an evaluation index to find the optimal solution,and the optimal solution is the decoupling signal with enhanced fault shock characteristics,fault characteristic frequency is extracted by frequency domain analysis.The experimental results show that this method can effectively enhance the early weak fault characteristics,and has the advantages of accurate calculation results and fast calculation speed.

关 键 词：故障特征增强 滚动轴承 WOA MOMEDA 排列熵 

分 类 号：TK407[动力工程及工程热物理—动力机械及工程] TP18[自动化与计算机技术—控制理论与控制工程]