个人信息
参与实验室科研项目
人机智能协同关键技术及其在智能制造中的应用
非可信智能驱动的可靠智造
学术成果
共撰写/参与撰写专利 1 项，投出/录用/发表论文 1 篇。
patent
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锡膏印刷机参数调整数据处理软件V1.0
许镇义,
刘斌琨,
康宇,
曹洋,
and 赵云波
2022
[PDF]
Conference Articles
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A Robustness Benchmark for Prognostics and Health Management
Binkun Liu,
Yun-Bo Zhao ,
Yang Cao,
Yu Kang,
and Zhenyi Xu
In 2022 41st Chin. Control Conf. CCC
2022
[Abs]
[doi]
[PDF]
With the rise of intelligent manufacturing, prognostics and health management(PHM) has developed rapidly as an important part of intelligent manufacturing.Existing deep learning-based PHM methods are data-dependent. However, sensor data often contains noise and is redundant and high-dimensional, making it difficult for the PHM methods to learn a stable set of model parameters, so the methods are likely to be wrong when disturbed. However, the factory hopes that the PHM methods are robust enough to adapt to various disturbances, so it is necessary to perform robustness evaluation on the existing methods in advance for easy deployment. Although the existing robust theoretical analysis methods for neural networks can obtain tight robust boundaries, they consume a lot of computing resources and are difficult to scale to large neural networks. To slove this problem, We design a benchmark for robustness analysis of large deep learning PHM models, in which we test the model robustness using a variety of perturbations to simulate the actual production environment of the factory. Specifically, Gaussian noise is used to test the robustness of the model to background noise; random mask is used to test the robustness of the model to data loss. We hope that our robustness benchmark can serve as a reference for designing PHM models to improve the robustness of factory PHM models.