...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Expert Systems with Application >A multi-objective optimization based deep feature multi-subspace partitioning method for process monitoring
【24h】

A multi-objective optimization based deep feature multi-subspace partitioning method for process monitoring

机译：A multi-objective optimization based deep feature multi-subspace partitioning method for process monitoring

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相关主题

摘要

Deep neural networks (DNNs) have shown advantages in dealing with complex nonlinear problems and have been applied in process monitoring. However, traditional DNN based process monitoring methods face the following problems. Firstly, only utilizing the features of the last layer to characterize industrial processes may lead to information loss. Secondly, due to the existence of irrelevant features, using all the deep features to establish a monitoring model may lead to the degradation of monitoring performance. Therefore, this paper proposes a multi-objective optimization based deep feature multi-subspace partitioning method for process monitoring. Firstly, a DNN model is established based on normal data, and all the deep features that can represent process information are extracted. Next, the residual is combined with the deep features to form a multi-level information representation vector. Then, aiming at the detection accuracy and detection diversity of the subspaces, the optimal multi-subspace partition can be solved with multi-objective optimization algorithm and the validation data (including normal data and fault data). Finally, the detection results of multiple sub-spaces are integrated through a fusion strategy to realize process monitoring. Applications in two industrial processes demonstrate the effectiveness and advantages of MOO-DFMSP.

著录项

获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号