基于非负矩阵分解与主元分析的时频图像识别方法研究

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Here, a method for machine condition identification was put forward based on non-negative matrix factorization （NMF） and principal component analysis （PCA）. A vibration signal was used to construct a Hilbert two-dimensional time frequency image after pre-proeessing. Then, NMF was used to determine the feature vector for the time frequency image. Principal component analysis （PCA） was used to reduce the dimension number of the feature vector, it was useful for three-dimension condition identification. Different condition identifications of rolling bearing were as examples to testify the effectiveness of this method. It was concluded that this method can improve accuracy of machine condition identification; it is helpful for machine fault diagnosis.应用非负矩阵分解与主元分析对时频图像处理，在此基础上进行设备状态识别。论述了对振动信号应用Hilbert谱构建二维时频图像，并用非负矩阵分解对时频图像构造特征向量，应用主元分析对提取的特征向量进行了降维处理，并在三维坐标系中进行表示和状态识别。以滚动轴承不同状态的识别为例，验证方法的有效性。研究表明此方法能够提高设备状态识别的准确性，有利于设备故障诊断的发展。