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摘要
Identifying fault categories, especially for compound faults, is a challenging task in mechanical fault diagnosis. For this task, this paper proposes a novel intelligent method based on dual-tree complex wavelet packet transform (DTCWPT) and multiple classifier fusion. In this method, in order to effectively extract underlying fault characteristic information, DTCWPT, which enjoys such attractive properties as nearly shift-invariance and reduced aliasing, is introduced and performed on vibration signals, and then an original feature set including time-domain and frequency-domain features is extracted from the frequency-band signals of DTCWPT to reveal machine conditions. In order to achieve the desired performance of fault classification, a new framework for multiple classifier fusion is developed on the basis of neighborhood rough set (NRS). The original feature set is firstly granulated into different granularity levels by NRS and different sensitive feature sets (SFSs) are obtained for reflecting the machine conditions from different perspectives. After that, the SFSs are respectively input into different classification algorithms to achieve the complementary advantages and substantive fusion of different classifiers. Finally, the classification results of multiple classifiers based on different classification algorithms and input SFSs are fused using Bayesian belief method to come up with the final diagnosis result. The proposed method is applied to the fault diagnosis of gearbox and locomotive roller bearings. The diagnosis results show that the proposed method is able to reliably identify the different fault categories which include both single fault and compound faults, which has a better classification performance compared to any one of the individual classifiers. Moreover, the validity of feature selection based on NRS and the superiority of feature extraction based on DTCWPT are also demonstrated by the testing results.
- 出版日期2016-1-1
- 单位西安交通大学
