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| A novel Incremental Semi-supervised VPMCD gear fault on-line diagnosis method |
| Yang Yu, Pan Haiyang, Li Yongguo,Cheng Junsheng |
| State key Laboratory of Advanced Design and Manufacture for Vehicle Body,Hunan University, Changsha, 410082 |
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Abstract Given the problem of fault samples is difficult to get and the demand of the real-time online diagnosis in the gear fault diagnosis. A novel Incremental Semi-supervised Variable Predictive Mode based Class Discriminate (ISVPMCD) gear fault on-line detection method is put forward in this paper. Firstly, the VPMCD approach was used to establish initial prediction model for a small number of labeled samples; secondly, the criterion of VPMCD was used to provide initial pseudo labels for unlabeled samples; thirdly, the pseudo labeled samples were screened by cross-correlation rule; fairly, the pseudo labeled samples and labeled samples as the training samples are to update the initial prediction model, so that the global information of the whole sample set could be considered, and which can effectively solve the problem of fault diagnosis of small sample. In addition, the method does not need to establish discriminant model in the process of real-time updating new samples, which shortens the time of classification and offers a new way for real-time online diagnosis. The analysis results of the UCI standard data and the experimental data of gear show that the ISVPMCD pattern recognition method suitable for small samples can identify the gear working state and fault type much more quickly and accurately.
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Received: 09 September 2013
Published: 25 April 2015
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