基于LSTM时间序列重建的生产装置异常检测

窦珊; 张广宇; 熊智华

doi:10.11949/j.issn.0438-1157.20181050

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基于LSTM时间序列重建的生产装置异常检测

过程系统工程 | 更新时间：2026-07-24

- 基于LSTM时间序列重建的生产装置异常检测
- Anomaly detection of process unit based on LSTM time series reconstruction
- 化工学报 2019年70卷第2期页码：481-486
- 作者机构：
  
  1.清华大学自动化系，北京100084
  2.浙江航天恒嘉数据科技有限公司，浙江嘉兴 314201
- 作者简介：
  
  窦珊(1994－)，女，硕士研究生，dous12@126.com
  熊智华（1971—），男，副教授，zhxiong@tsinghua.edu.cn
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20181050
  中图分类号： TP 391
- 收稿：2018-09-19，
  
  修回：2018-10-18，
  
  网络首发：2020-06-16，
  
  纸质出版：2019-02-05
- 稿件说明：
移动端阅览
窦珊, 张广宇, 熊智华. 基于LSTM时间序列重建的生产装置异常检测[J]. 化工学报, 2019, 70(2): 481-486

DOU Shan, ZHANG Guangyu, XIONG Zhihua. Anomaly detection of process unit based on LSTM time series reconstruction[J]. CIESC Journal, 2019, 70(2): 481-486
窦珊, 张广宇, 熊智华. 基于LSTM时间序列重建的生产装置异常检测[J]. 化工学报, 2019, 70(2): 481-486 DOI： 10.11949/j.issn.0438-1157.20181050.

DOU Shan, ZHANG Guangyu, XIONG Zhihua. Anomaly detection of process unit based on LSTM time series reconstruction[J]. CIESC Journal, 2019, 70(2): 481-486 DOI： 10.11949/j.issn.0438-1157.20181050.

摘要

工业生产装置通常设置传感器报警阈值进行报警，但是对处于报警阈值以下的时间序列异常难以及时捕捉。基于统计的传统检测方法在解决时间序列异常检测上存在很大挑战，因此提出基于long short term memory (LSTM)时间序列重建的方法进行生产装置的异常检测。该算法首先引入一层LSTM网络对传感器数据的时间序列进行向量表示，采用另一层LSTM网络对时间序列进行逆序重建，然后利用重建值与实际值之间的误差，通过极大似然估计方法对该段序列进行异常概率估计，最终通过学习异常报警阈值实现时间序列异常检测。采用ECG测试数据、能源数据与危险品储罐传感器数据进行了仿真实验，验证了所提方法在不同长度的数据上的有效性。

Abstract

Industrial production equipment usually sets sensor alarm thresholds for alarms

but it is difficult to capture time series abnormalities below the alarm thresholds. The traditional statistics based detection method has great challenges in these time series anomaly detection. In this paper

an approach to the anomaly detection of process units is proposed by using the long short term memory (LSTM) time series reconstruction. At first

an LSTM network is introduced to vectorize the time series of sensor data

and another LSTM network is utilized to reconstruct the time series in reverse sequence. Then

the errors between the reconstructed values and the actual values are used to estimate the anomaly probability by the maximum likelihood estimation method. Eventually

anormaly detection is achieved by learning the abnormal alarm thresholds. Simulation resutls on the ECG standard testing data

energy data and sensor data of the dangerous goods tank have shown the effectiveness of the proposed method on data with different lengths.

关键词

Keywords

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