摘要
针对基于时域组合特征的故障诊断方法的不足,提出一种基于小波包能谱熵分析的液压油缸内泄漏故障诊断方法。分析无杆腔压力信号的时域特征,采用小波包变换提取压力信号的能谱熵并输入到改进LM神经网络进行内泄漏的故障诊断。实验结果表明,无泄漏压力信号的能谱熵向量各元素分布较均匀;而泄漏信号的能谱熵向量各元素差异较大;改进LM神经网络在精度、准确率等方面高于传统BP、LM神经网络。与时域组合特征法进行比较,结果验证算法的高效可检测性。以不同分类器、不同小波基对算法诊断性能的影响进行分析,结果表明,该方法具有很强的稳定性和优越性。
In view of the disadvantage of the fault diagnosis method based on time domain feature in which the required lighter leakage is similar to no leakage in time domain .A new fault diagnosis method is proposed .This method analyzes the time domain feature which is extracted from the pressure signal of hydraulic cylinder ,and then ,a wavelet packet decomposition method is adopted to extract the energy spectrum entropy from pressure signal at different scales .Lastly ,the energy spectrum entropy is input into the optimized LM neural network analyzer to identify no leakage , slighter leakage and heavy leakage .Experimental results show that each element of the spectrum entropy vector of normal signal of no leakage is evenly distrituted ,while the element of fault signal spectrum entropy vector is remarkably regularly changed . The accuracy and precision of the optimized LM neural network are higher than those of the traditional neural network model .The proposed method is compared to method based on time domain feature ,and the results validate the correctness of design idea and the high-efficiency detectability of algorithm . The influence of wavelet base and classifier on the diagnosis performance of the proposed method is analyzed ,and the results show that the proposed method has very high stability and robustness .
出处
《实验技术与管理》
CAS
北大核心
2013年第10期59-64,共6页
Experimental Technology and Management
基金
国家自然科学基金资助项目(60974012
61171160)
