基于跨域流形正则化特征域适应的浮选工况识别
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东北大学

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中图分类号:

TP273

基金项目:

国家重点研发计划(2021YFF0602404)、国家自然科学基金(61873053、61621004)


Identification of flotation working condition based on feature domain adaptation of cross-domain manifold regularization
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Affiliation:

Northeastern University

Fund Project:

National Key R&d Program of China (2021YFF0602404), National Natural Science Foundation of China (61873053, 61621004)

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    摘要:

    针对浮选过程的故障工况信息不足难以建立准确识别模型,导致调整浮选生产工况不及时,从而无法正常稳定运行的问题,本文提出一种基于跨域流形正则化特征域适应方法。该方法将已有相似完备浮选过程积累的丰富工况信息作为源域迁移到未建模的不完备浮选过程的目标域中,首先通过最大域内类密度和局部流形正则化约束分别保留原始判别信息和维持域内邻域结构信息不变,从而提取完备工况和不完备工况域间的特征并投影到公共子空间中,其次由最大均值差异缩小源域和目标域间分布差异,建立分类识别模型,再结合D-S证据理论,融合浮选过程泡沫的静态特征与动态特征信息,提高对不完备浮选过程工况识别的泛化能力,保证得到较好的识别分类效果。最后通过仿真实验验证了所提方法的有效性。

    Abstract:

    Aiming at the problem that the flotation process is difficult to establish an accurate identification model due to Insufficient fault condition information, which leads to the delay in adjusting the flotation production conditions and the failure to run statically in the normal state, this paper proposes a feature transfer learning method based on cross-domain manifold regularization(CDMRFTL). In this method, the rich and complete working condition information accumulated by the existing similar complete flotation process is transferred as the source domain to the target domain of the incomplete unmodeled flotation process. First, extracting the common features between the complete working conditions and the incomplete working conditions and mapping them to the common subspace through the largest domain class density and partial manifold regularization, to constraint discriminant information and retain the original unchanged in neighborhood domain structure information, Secondly narrowing the difference of distribution between source domain and target domain by the largest average differences, and the classification and recognition model was established, Next D-S evidence theory was combined to fuse the apparent characteristics and depth characteristics of the foam in the flotation process to improve the generalization ability of incomplete flotation process condition recognition and ensure a better recognition and classification effect. Finally, the effectiveness of the proposed method was verified by simulation experiments.

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  • 收稿日期:2021-09-13
  • 最后修改日期:2022-03-15
  • 录用日期:2022-03-15
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