基于TSMAAPE与WOA-KELM的液压泵故障诊断  

Fault Diagnosis of Hydraulic Pump Based on TSMAAPE and WOA-KELM

作　　者：李琨[1] 张久亭 LI Kun;ZHANG Jiuting(School of Information Engineering and Automation,Kunming University of Science and Technology,Kunming Yunnan 650500,China)

机构地区：[1]昆明理工大学信息工程与自动化学院,云南昆明650500

出　　处：《机床与液压》2022年第9期201-209,共9页Machine Tool & Hydraulics

摘　　要：多尺度排列熵(MPE)是一种非线性动力学方法,广泛应用于旋转机械的故障诊断。然而,排列熵没有考虑具有相同排列模式的时间序列可能具有不同的振幅,并且粗粒化方法存在缺陷。为解决上述问题,提出时移多尺度振幅感知排列熵(TSMAAPE)。利用时移时间序列改善MPE中粗粒度时间序列存在的不足,同时引入振幅感知排列熵。通过与时移多尺度排列熵和多尺度振幅感知排列熵进行对比,验证TSMAAPE的鲁棒性。考虑到TSMAAPE在特征提取方面的优势,结合鲸鱼优化算法优化的核极限学习机,提出一种液压泵智能故障诊断方法。结果表明:该方法对液压泵的不同故障具有较好的分类准确率,在故障诊断领域有广阔的应用前景。Multi-scale permutation entropy(MPE)is a nonlinear dynamic method,which has been widely used in fault diagnosis of rotating machinery.However,in permutation entropy,it does not considered that time series with the same permutation pattern may have different amplitudes,and the coarse-grained method has defects.In order to solve the above problems,a time-shift multi-scale amplitude aware permutation entropy(TSMAAPE)was proposed.Time-shift time series was used to improve the shortcomings of coarse-grained time series in MPE and amplitude-aware permutation entropy was introduced.The robustness of TSMAAPE was verified by comparing with time-shift multi-scale permutation entropy and multi-scale amplitude aware permutation entropy.Taking into account the advantages of TSMAAPE in feature extraction,combined with the kernel extreme learning machine optimized by the whale optimization algorithm,an intelligent fault diagnosis method for hydraulic pumps was proposed.The results show that this method has good classification accuracy for different faults of hydraulic pumps,and has broad application prospects in the field of fault diagnosis.

关 键 词：液压泵 故障诊断 时移多尺度振幅感知排列熵 核极限学习机 

分 类 号：TH165.3[机械工程—机械制造及自动化]