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Objected to fault diagnosis of planetary gearbox, the research and implementation of classification model on small sample with semi-supervised learning and DPN in this paper is carried out. Firstly, the acceleration sample data for four status of the planetary gearbox are obtained, which are including the normal, internal ring gear fault, sun gear fault and Coupling fault between planetary gear and bearing. And the feature vector is built with characteristic parameters such as average amplitude, kurtosis, root mean square, root square amplitude, form factor, crest factor and margin factor. Then the data is delt with CEEMD method for noise reduction and continually the parameters are computed. Then the vector is as input of DBN and Semi-supervised Learning algorithm to fault diagnosis for planetary gear system. Also the comparison competition are done by using DBN and SVM. The results show that under the small sample data, the method of CEEMD - DBN could be more effective under small sample data. The research could provide an effective method for the diagnosis and classification of small sampling projects.
Inspec keywords: mechanical engineering computing; mean square error methods; rings (structures); machine bearings; supervised learning; signal denoising; Hilbert transforms; belief networks; fault diagnosis; gears; unsupervised learning
Subjects: Mechanical components; Mechanical engineering applications of IT; Numerical analysis; Signal processing and detection; Supervised learning; Other topics in statistics; Statistics; Unsupervised learning; Other topics in statistics; Civil and mechanical engineering computing; Integral transforms in numerical analysis; Maintenance and reliability; Integral transforms in numerical analysis; Digital signal processing; Mechanical drives and transmissions