期刊
NEUROCOMPUTING
卷 168, 期 -, 页码 119-127出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2015.06.008
关键词
Deep learning; Support vector classification; Multimodal homologous feature; Gearbox; Fault diagnosis
资金
- Secretariat for Higher Education, Science, Technology and Innovation of the Republic of Ecuador
- National Natural Science Foundation of China [51375517]
- Chongqing Innovation Team in University [KJTD201313]
- Youth Science and Technology Talents Project of Chongqing [cstc2014kj-rc-qnrc00003]
Gearboxes are crucial transmission components in mechanical systems. Fault diagnosis is an important tool to maintain gearboxes in healthy conditions. It is challenging to recognize fault existences and, if any, failure patterns in such transmission elements due to their complicated configurations. This paper addresses a multimodal deep support vector classification (MDSVC) approach, which employs separation-fusion based deep learning in order to perform fault diagnosis tasks for gearboxes. Considering that different modalities can be made to describe same object, multimodal homologous features of the gearbox vibration measurements are first separated in time, frequency and wavelet modalities, respectively. A Gaussian-Bernoulli deep Boltzmann machine (GDBM) without final output is subsequently suggested to learn pattern representations for features in each modality. A support vector classifier is finally applied to fuse GDBMs in different modalities towards the construction of the MDSVC model. With the present model, deep representations from wide modalities improve fault diagnosis capabilities. Fault diagnosis experiments were carried out to evaluate the proposed method on both spur and helical gearboxes. The proposed model achieves the best fault classification rate in experiments when compared to representative deep and shallow learning methods. Results indicate that the proposed separation-fusion based deep learning strategy is effective for the gearbox fault diagnosis. (C) 2015 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据