1. Mechanical fault diagnose of diesel engine based on bispectrum and Support Vector Machines
- Author
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Jianmin Mei, Li Tang, Yunkui Xiao, Huimin Zhao, Huafei Huang, and Ruili Zeng
- Subjects
Noise (signal processing) ,Feature (computer vision) ,Computer science ,Speech recognition ,Diagonal ,Feature extraction ,Fault (power engineering) ,Parallel ,Bispectrum ,Signal ,Algorithm - Abstract
The vibrant signal of diesel engine is analyzed by the method of bispectrum, and bispectral feature planes are searched along diagonal and parallel lines of diagonal at certain step in the bispectral modulus field, and the mean breadth value in bispectral feature planes are calculated as signal feature which is capable of describing the fault. The Support Vector Machines is used to diagnose the fault successfully by importing signal features as training samples. The experiment results show that, the noise in the vibrant signal of diesel engine can be eliminated by bispectrum and the signal feature can be extracted effectively; The signal features are perfectly described by feature planes and exist not only on diagonal and but also in the field besides the diagonal; Support Vector Machines can study effectively and diagnose successfully with limited fault samples.
- Published
- 2009
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