- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | ZHANG Jiangquan, GAO Hongli, XIANG Shoubing, GUO Liang, TAN Yongwen. Intelligent Evaluation Method for Ball Screw Degradation State[J]. Journal of Southwest Jiaotong University, 2022, 57(4): 813-820. doi: 10.3969/j.issn.0258-2724.20220082
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The existing ball screw degradation assessment method usually assumes that sufficient labeled data sets are available. However, it is difficult to obtain massive labeled data sets under practical projects due to excess failure cost and difficulty of obtaining labels. To solve the above problems, an intelligent state evaluation method based on multi-scale adversarial domain adversarial learning is proposed. Combining an attention convolution neural network module and a domain adversarial learning module, a deep learning model is established by using sensor signals collected under different working conditions, so as to learn domain invariant features adaptively and realize efficient knowledge reuse and feature migration. The experimental data sets are constructed by using the ball screw degradation signals collected under multiple working conditions to verify the effectiveness of the method. The results show that the proposed method achieves a recognition accuracy higher than 89.02% in six sub-tasks of degradation state identification of ball screw under cross-working conditions with missing labels. The proposed method can fully migrate key features with labeled data and achieve the degradation state identification of target operating conditions under missing label samples.
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