4.7 Article

Intelligent Diagnostics for Ball Screw Fault Through Indirect Sensing Using Deep Domain Adaptation

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2020.3043512

关键词

Ball screw; deep learning; fault diagnosis; parallel data (PD); transfer learning

向作者/读者索取更多资源

The study proposed an indirect sensing method to address the challenge of impractical sensor locations for ball screws. By utilizing a convolutional neural network-based domain adaptation method, the transferability of the diagnostic model between sensor locations was achieved, resulting in satisfactory accuracy across multiple transfer tasks.
Intelligent data-driven fault diagnostics for rotating machinery is well established. However, ball screws pose a unique challenge of impractical sensor locations for long-term deployment due to their complex motion trajectory and sophisticated mechanical structure. To overcome this challenge, an indirect sensing method is proposed. While techniques are available for multiple transfer learning tasks, cross-sensor domain adaptation remains unexplored. Thus, a convolutional neural network-based domain adaptation method is proposed that minimizes the maximum mean discrepancy of high-level representations between domains and exploits novel parallel data to attain class-level alignment. Proposed method achieved a mean testing accuracy of 98.25% upon validation on 33 transfer tasks designed across five accelerometer locations on a ball screw testbed depicting nine health conditions through variations in preload levels and backlash. This convenience of transferability of the diagnostic model between sensor locations can go a long way in robust and reliable condition monitoring of critical assets.

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。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据