Decision tree and PCA-based fault diagnosis of rotating machinery

• Published in: Mechanical systems and signal processing Vol. 21; no. 3; pp. 1300 - 1317
• Main Authors: Sun, Weixiang, Chen, Jin, Li, Jiaqing
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 01.04.2007; Elsevier
• Subjects: Applied sciences; C4.5; Data mining; Decision tree; Exact sciences and technology; Fault diagnosis; Fundamental areas of phenomenology (including applications); Industrial metrology. Testing; Measurement and testing methods; Mechanical engineering. Machine design; Physics; Principal component analysis; Rotating machinery; Solid mechanics; Structural and continuum mechanics; Vibration, mechanical wave, dynamic stability (aeroelasticity, vibration control...); Fault diagnosis; Rotating machinery; Data mining; Decision tree; C4.5; Principal component analysis; Data analysis; Vibration; Rotating machine; Fault tree; Decision making; Unbalanced conditions; Data processing; Information extraction; Pattern recognition; Modeling; Backpropagation algorithm; Shaft; Database; Signal processing; Fault diagnostic; Pattern extraction; Cracked beam

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, pre...