基于能量信号分析的齿轮传动系统故障诊断方法与系统研究

发布时间：2018-03-27 18:05

  本文选题：故障诊断　切入点：能量信号　出处：《华南理工大学》2014年博士论文

【摘要】：开展创新性的机械故障诊断技术研究，对于提升设备的安全稳定运行品质具有重要意义。本文基于能量视角，以论证齿轮传动系统振动与输入能量之间的相关性为开篇，挖掘潜藏于能量信号之中的故障模式规律，在研究有效的能量信号非线性处理、特征提取以及故障模式模糊识别等方法的过程中，建立一种新型的面向齿轮传动系统等旋转机械的故障诊断方法。 首先，对齿轮传动系统的振动机理进行了研究，通过对齿轮静态传递误差变量的理论分析，揭示了输入瞬时能量与齿轮传动产生的振动位移偏差量之间的映射关系。同时采用频域相干分析方法对故障时的输入功率和振动信号的相干性做了实验分析，证明二者之间具有强相关性、能量信号也可表征故障信息。这些论证性研究为后续工作的合理性提供了理论依据。 其次，研究了HHT的改进方法，建立了用于抑制HHT中端点效应的PSO-ARMA波形延拓预测模型。模型建模时首先提出并研究了基于粒子熵的参数自适应变异粒子群算法(EPPSO算法)，再将其运用到ARMA模型的参数优化估计中，依据矩估计法得到的初值在参数解空间内全局搜索，最终得到ARMA模型的最佳参数。同时应用该模型进行了端点效应抑制仿真，结果显示EMD分解后各IMF的波形完整度较高、有效降低了谱线能量泄漏。模型为后续准确提取系统输入能量信号的时频特征提供了技术支撑。 再次，研究了齿轮传动系统故障特征提取方法。开展了能量信号HHT分析实验研究：分别针对正常和断齿状态下的能量信号进行EMD分解，并对振动和能量信号的Hilbert谱和边际谱对比分析，验证了能量信号分析结果在表征故障状态方面的优越性。随之研究了故障特征向量库建立方法，构建了含前6阶IMF的归一能量、偏度、峰度、标准差和近似熵等参数在内的多维故障特征向量，为后续开展故障特征识别方法研究奠定基础。 然后，提出并研究了一种新颖的基于核主元熵模糊聚类的故障识别方法，建立了相应的KEFKM模型。模型涵盖3大功能：KPCA特征参数降维；基于核主元熵的核特征模糊聚类；基于模糊关联熵的故障模糊识别。1)首先研究了故障数据模糊聚类方法，，提出了主元信息熵的概念，建立了将其和FKM算法相融合的新模糊聚类方法：运用KPCA对数据降维以减少运算量，运用核密度估计和最大熵原理，先对第一核主元数据聚类以获取最佳分类数和初始聚类中心，再针对核主元特征进行聚类。实验结果显示该方法可显著提高故障数据的聚类效果。2)接着提出了针对待检样本的基于模糊关联熵的故障模糊识别规则。通过实验对比分析了最大贴近度和模糊关联熵方法之间的差异，指出模糊关联熵可以面向数据整体来衡量待识别数据的分布特点，对判断两个模糊子集的相似度具有显著作用。3)最后应用KEFKM模型开展了齿轮实验研究，结果证明模型可以保证训练样本的显著核主元经模糊聚类后可形成类间、类内分布均合理的样本空间；基于模糊关联熵的故障模式模糊识别方法(规则)在处理故障样本模式识别方面表现优秀，验证了KEFKM模型的有效性。 最后，围绕齿轮传动系统能量信号监测与故障诊断系统的设计展开了研究。分析了系统的基本结构及信息获取、处理等环节的实现过程，初步开发了故障诊断系统，系统基于虚拟仪器技术，通过内嵌Matlab实现能量信号分析、故障模式模糊识别等步骤，最终实现故障状态实时监测。同时研究了无线传感网络技术在齿轮传动监测中的应用，设计了基于WSN的状态监测节点，通过优化Zigbee协议栈，实现了节点自组网络，可面向齿轮监测实现安全、便捷的远程数据采集。
[Abstract]:Research on the mechanical fault diagnosis technology innovation, has important significance for the safe and stable operation to improve the quality of equipment. Based on the energy point of view, to demonstrate correlation between gear transmission system vibration and energy input for the opening, mining patterns hidden in fault signal energy, in the process of nonlinear signal energy effective research, process characteristics extraction and fault mode fuzzy recognition method, the fault diagnosis method for the establishment of a new type of gear transmission system of rotating machinery.
First of all, the mechanism of vibration of gear transmission system are studied by the static transmission error of gear variable theory analysis, reveals the mapping relationship between the vibration displacement deviation and input instantaneous energy generated by gear transmission. At the same time using frequency coherent coherence analysis method for input power and the fault vibration signal of the experimental analysis has been done and there is a strong correlation between the two, the energy signal can also characterize the fault information. These arguments for subsequent research on the rationality of the work provides a theoretical basis.
Secondly, the improved method of HHT, for the PSO-ARMA waveform to inhibit the end effects of HHT in the extended forecasting model is established. First proposed and studied the adaptive mutation particle swarm algorithm based on Entropy Modeling (EPPSO algorithm), and then apply it to the ARMA model parameter estimation, on the basis of moment estimation method to get the initial value in the solution space of global search parameters, and ultimately get the best parameters of the ARMA model. At the same time to restrain the end effect simulation using the model, the results show that EMD decomposition after each IMF waveform integrity is high, effectively reducing the spectrum energy leakage. The model provides the technical support for the subsequent accurate extraction of time-frequency characteristics system input signal.
Again, the gear fault feature extraction method of the transmission system. The experiment was carried out to study the energy signal HHT analysis respectively according to the energy signal of normal and broken teeth under the condition of EMD decomposition, and the vibration and energy signal Hilbert spectrum and marginal spectrum analysis, signal analysis results can verify the superiority in the characterization of fault state the. Then the paper studies the method of building fault feature vector library, constructed by the first 6 order IMF normalized energy, skewness, kurtosis, standard deviation and approximate entropy and other parameters, the multi fault feature vector, which lays the foundation for the follow-up research methods of avoidance feature recognition.
Then, this paper presents a novel kernel principal component entropy fuzzy clustering based on fault identification method, established the corresponding KEFKM model. The model covers 3 functions: KPCA feature dimension reduction; kernel feature kernel principal component entropy fuzzy clustering based on fuzzy.1; fault recognition based on Fuzzy Association entropy) first study on the fault data of fuzzy clustering method, put forward the concept of the main element of information entropy, a new method combining the fuzzy clustering and FKM algorithm: the use of KPCA to reduce the dimension of data in order to reduce the amount of computation, using kernel density estimation and maximum entropy principle, the first nuclear main metadata clustering in order to obtain the best classification number and the initial cluster center, then clustering for kernel principal component characteristics. Experimental results show that this method can significantly improve the clustering effect of.2 fault data) and then put forward the fuzzy entropy for fault fuzzy association based on the sample to be detected The rules of recognition. Comparing to the difference between fuzzy association degree and entropy method, pointed out the distribution characteristics of fuzzy entropy can be measured to identify the overall data oriented data,.3 has a significant role to judge the similarity of two fuzzy subsets) should end with KEFKM model experiment were carried out to study the gear, the result shows that the model to ensure that the main element was nuclear training samples by fuzzy clustering analysis can be formed between classes, class distribution are reasonable sample space; fuzzy recognition method of fault pattern based on Entropy Fuzzy Association (rules) with excellent performance in dealing with pattern recognition of fault samples, verifies the effectiveness of the KEFKM model.
鏈

本文编号：1672633