基于多采样序列特征提取网络的多变量间歇过程故障预测  

Fault prediction of multivariate batch process based on multi-sampled sequence feature extraction network

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作  者:高学金[1,2,3,4] 李博伦 韩华云 高慧慧 齐咏生 GAO Xuejin;LI Boun;HAN Huayun;GAO Huihui;QI Yongsheng(School of Information Science and Technology,Beijing University of Technology,Beijing 100124,China;Engineering Research Center of Digital Community,Ministry of Education,Beijing 100124,China;Beijing Laboratory for Urban Mass Transit,Beijing 100124,China;Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing 100124,China;School of Electric Power,Inner Mongolia University of Technology,Hohhot 010051,Inner Mongolia,China)

机构地区:[1]北京工业大学信息科学技术学院,北京100124 [2]数字社区教育部工程研究中心,北京100124 [3]城市轨道交通北京实验室,北京100124 [4]计算智能与智能系统北京市重点实验室,北京100124 [5]内蒙古工业大学电力学院,内蒙古呼和浩特010051

出  处:《化工学报》2024年第12期4629-4645,共17页CIESC Journal

基  金:北京市自然科学基金项目(4222041);国家自然科学基金青年基金项目(62303026);国家自然科学基金项目(62241309)。

摘  要:故障预测可以指示变量的异常变化,提前预测故障情况。现有故障预测方法仅考虑完整序列的全局时间依赖关系,忽略了变量间依赖关系及采样子序列中不同的局部时间依赖关系。针对上述问题,提出了一种基于多采样序列特征提取网络(multi-sampled sequence feature extraction network,MSFEN)的故障预测架构。首先设计了一种批次联合嵌入机制,在考虑批次周期性的同时更好地表达变量间依赖关系。然后,开发了一种序列采样机制划分完整时间序列与不同尺度的采样子序列。之后,分别设计了翻转平滑Transformer与卷积交互提取模块,以全面地提取多尺度时间依赖关系与变量间依赖关系。最后,融合多采样序列特征获得最终的编码特征,通过前馈层实现故障预测。利用青霉素发酵过程进行实验,结果表明该方法具有良好的故障预测性能。Fault prediction can indicate abnormal changes in variables and predict fault conditions in advance.Existing fault prediction methods primarily consider the global temporal dependencies of the complete sequence,which neglecting the dependencies between variables and the distinct local temporal features in the sampled subsequences.To address the above issues,a fault prediction architecture based on multi-sampled sequence feature extraction network(MSFEN)is proposed.First,a batch joint embedding mechanism is designed to better express the dependencies between variables while considering batch periodicity.Then,a sequence sampling mechanism is developed to divide the complete time series into sampled subsequences of different scales.Subsequently,the invert smoothing Transformer and the convolutional interactive extraction module are designed to comprehensively extract multi-scale temporal dependencies and variable dependencies.Finally,the multi-sampled sequence features are fused to obtain the final encoding features,and fault prediction is achieved through the feed forward layer.Experiments are conducted using the penicillin fermentation process,and the results show that this method has good fault prediction performance.

关 键 词:间歇式 故障预测 序列采样 神经网络 多尺度 

分 类 号:TP277[自动化与计算机技术—检测技术与自动化装置]

 

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