Decision tree and PCA-based fault diagnosis of rotating machinery

作者：Sun, Weixiang^*; Chen, Jin; Li, Jiaqing

来源：Mechanical Systems and Signal Processing, 2007, 21(3): 1300-1317.

DOI：10.1016/j.ymssp.2006.06.010

• fault diagnosis
• rotating machinery
• decision tree
• C4.5
• data mining
• principal component analysis

After analysing the flaws of conventional fault diagnosis methods, data mining technology is introduced to fault diagnosis field, and a new method based on C4.5 decision tree and principal component analysis (PCA) is proposed. In this method, PCA is used to reduce features after data collection, preprocessing and feature extraction. Then, C4.5 is trained by using the samples to generate a decision tree model with diagnosis knowledge. At last the tree model is used to make diagnosis analysis. To validate the method proposed, six kinds of running states (normal or without any defect, unbalance, rotor radial rub, oil whirl, shaft crack and a simultaneous state of unbalance and radial rub), are simulated on Bently Rotor Kit RK4 to test C4.5 and PCA-based method and back-propagation neural network (BPNN). The result shows that C4.5 and PCA-based diagnosis method has higher accuracy and needs less training time than BPNN.

• 出版日期2007-4
• 单位上海交通大学