高学金^1,2,3,4,刘爽爽^1,2,3,4,高慧慧^1,2,3,4

• 1:北京工业大学 信息学部，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

摘要(Abstract)：

针对传统故障监测方法难以提取数据深层特征的问题，提出一种基于多变量深度卷积神经网络的故障监测方法，以提高监测精度。为捕获过程动态性，采用滑动窗技术对过程变量序列进行分割，利用希尔伯特-黄变换对分割后的序列进行分解，得到时频图，有效挖掘变量序列在幅值、频率、相位上的异常变化信息；以时频图为输入，基于深度卷积神经网络构建故障监测模型，提取故障信息深层特征，提高监测精度；利用青霉素发酵过程仿真数据和大肠杆菌生产数据进行实验验证，结果表明所提方法监测精度分别高达95%和93%以上。

关键词(KeyWords)： 卷积神经网络;希尔伯特-黄变换;特征提取;发酵过程;故障监测

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金（61803005，61640312，61763037）；北京市自然科学基金（4172007，4192011）；山东省重点研发计划（2018CXGC0608）；北京市教育委员会资助。

作者(Author): 高学金^1,2,3,4,刘爽爽^1,2,3,4,高慧慧^1,2,3,4

DOI: 10.3969/j.issn.1003-9015.2020.06.023

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