曾峰荣¹,孙焕琪¹,熊伟丽^1,2

• 1:江南大学 物联网工程学院， 江苏 无锡 214122
• 2:江南大学 轻工过程先进控制教育部重点实验室， 江苏 无锡 214122

摘要(Abstract)：

针对工业过程数据的多尺度时序特征提取困难问题,提出一种基于特征融合与时间卷积自编码器的故障检测方法。首先,采用多层时间卷积网络结构,从不同尺度对输入时间序列进行特征提取,构建多尺度的时间卷积自编码器;其次,设计基于高效通道注意力的特征融合模块,通过跳跃连接加入时间卷积自编码器中,该模块对不同尺度的时序特征进行跨通道连接,生成相应权重对特征进行加权融合,从而捕获更丰富的时序信息,增强模型对正常序列与异常序列重构误差的区分度;最后,通过重构误差建立统计量,采用核密度估计确定控制限从而实现故障检测。将所提检测方法应用于数值案例和田纳西-伊士曼过程,实验结果证明了该方法具有良好的故障检测性能,能为复杂工业过程故障检测提供一定参考。

关键词(KeyWords)： 故障检测;时间卷积网络;自编码器;注意力机制;特征融合

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金(61773182);国家重点研发计划子课题资助项目(2018YFC1603705-03)。

作者(Author): 曾峰荣¹,孙焕琪¹,熊伟丽^1,2

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