Abstract：In this paper, a method for machine condition classification is put forward based on Non-negative matrix factorization (NMF) and Principal component analysis (PCA). Vibration signal is used to construct Hilbert two-dimensional time frequency image after pre-processing. Then, NMF is used to determine feature vector for time frequency image. Principal component analysis (PCA) is used to reduce dimension for feature vector, which will be useful for three-dimension condition classification. Rolling bearing different conditions classification is as an example to testify the effectiveness of this method. It can be concluded that this method can improve accuracy for machine condition classification. It will contribute the development for machine fault diagnosis.
陈李宏坤;禹臻;周帅;张志新. 基于非负矩阵分解与主元分析的时频图像识别方法研究[J]. , 2012, 31(18): 169-172.
Li Hong-Kun;Chen Yu-Zhen;Zhou Shuai;Zhang Zhi-Xin. Investigation on Time-Frequency Image Classification by Using Non-Negative Matrix Factorization and Principal Component Analysis. , 2012, 31(18): 169-172.
2012, Vol. 31 
