化工学报2019，Vol.70Issue(2)：481-486,6.DOI:10.11949/j.issn.0438⁃1157.20181050
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
窦珊 1张广宇 2熊智华1
作者信息
- 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
- 折叠
摘要
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
算法/神经网络/参数估计/LSTM/时间序列/异常检测/极大似然估计Key words
algorithm/ neural networks/ parameter estimation/ LSTM/ time series/ anomaly detection/ maximum likelihood estimation分类
信息技术与安全科学引用本文复制引用
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):481-486,6.
