Abstract:Aiming at the problem of fault data classification of rotating machinery intelligent decision-making technology, a dimensionality reduction algorithm of rotor fault data set based on Standard Orthogonal Discriminant Projection (SODP) was proposed. Firstly, the original fault feature set is constructed from time domain, frequency domain and time-frequency domain, and the vibration signal was transformed into a high-dimensional feature data set, and then IODP was used to select the subset sensitive feature that best reflect the nature of the fault. Finally, the Low-dimensional feature subsets were input into the KNN classifier for fault pattern identification. The vibration signal set of a double-span rotor system were used to verify the method. It was proved that the method can extract global and local discriminant information comprehensively, which makes the difference between fault categories clearer and corresponding fault pattern recognition accuracy rate improved. The research shows that the algorithm can provide reference for actual rotor fault diagnosis.
石明宽,赵荣珍. 基于标准正交判别投影的转子故障数据集降维方法[J]. 振动与冲击, 2020, 39(18): 96-102.
SHI Mingkuan,ZHAO Rongzhen. Dimension reduction of a rotor faults data set based on standard orthogonal discriminant projection. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(18): 96-102.
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