CIESC Journal ›› 2019, Vol. 70 ›› Issue (2): 481-486.doi: 10.11949/j.issn.0438-1157.20181050

• Process system engineering • Previous Articles     Next Articles

Anomaly detection of process unit based on LSTM time series reconstruction

Shan DOU1(),Guangyu ZHANG2,Zhihua XIONG1()   

  1. 1. Department of Automation, Tsinghua University, Beijing 100084, China
    2. Zhejiang Aerospace Hengjia Data Technology Co., Ltd., Jiaxing 314201, Zhejiang, China
  • Received:2018-09-19 Revised:2018-10-18 Online:2019-02-05 Published:2018-12-04
  • Contact: Zhihua XIONG E-mail:dous12@126.com;zhxiong@tsinghua.edu.cn

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.

Key words: algorithm, neural networks, parameter estimation, LSTM, time series, anomaly detection, maximum likelihood estimation

CLC Number: 

  • TP 391

Fig.1

LSTM memory block"

Fig.2

Overall structure"

Table 1

Procedure of MLE parameter estimation[25]"

1.设异常概率p随重建误差e呈高斯分布,则其似然函数如式(4)所示;

2.对似然函数取对数并求和,整理后为

H(μ,Σ)=eiV1lnp(ei|μ,Σ)(8)

3.对Hμ,Σ求偏导,使得其满足:

?H(μ,Σ)?μ=0?H(μ,Σ)?Σ=0

4.求解上述方程得到参数估计值如下所示,其中N为数据集NV1的时间点总数。

μ=1Ni=1NeiΣ=1Ni=1N(ei-μ)(ei-μ)T(9)

Fig.3

ECG standard test data"

Fig.4

Energy consumption data"

Fig.5

Dangerous goods storage sensor data"

Table 2

Experimental results"

DataPRF0.1
RNNLSTMRNNLSTMRNNLSTM
ECG0.981.000.450.430.960.97
EN0.890.940.100.120.830.88
SE0.890.920.200.260.860.89
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