液压泵故障诊断稀疏编码方法研究

针对液压泵故障特征难以提取、诊断过程复杂、自动化程度低等特点，将稀疏编码方法应用于液压泵故障诊断.通过对液压泵泵壳处振动信号进行时频域变换，将变换后的信号作为样本，采用K-SVD算法对训练样本进行字典学习以获取字典，利用正交匹配追踪算法对测试信号进行分解与重构，通过不同类别字典对测试信号的重构率大小进行故障种类识别，实现液压泵故障分类.通过试验验证并与BP神经网络、支持向量机对比，结果表明稀疏编码方法具有对故障识别速度快、准确率高、稳定性好等优点，可以有效地实现对液压泵故障的诊断. 

A Fault Diagnosis Method Based on Sparse Coding for Hydraulic Pump

Due to the problems existing in the process of hydraulic pump fault diagnosis, the difficulty and the complexity to extract weak feature of the failure hydraulic pump and to automate, a sparse coding method was proposed for the hydraulic pump fault diagnosis. Firstly, the vibration signals were demodulated and transformed to frequency domain, then the K-SVD algorithm was used to obtain the dictionary from the learning of training samples, at last, the orthogonal matching pursuit algorithm was used to decompose and reconstruct the test signals. The classification of the failure of the hydraulic pump was achieved according to the reconstruction rate of the testing signal in different types of dictionary. Compared with BP neural network and support vector machine (SVM), the proposed sparse coding method shows a faster recognition speed, higher accuracy and better stability, and it can realize the fault diagnosis of hydraulic pump effectively.