An LSTM-based Motion Recognition Method for Power Operation and Maintenance

Authors

Peizhen Liu, 1.School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China; ;
Yuxiang Jia, 1.School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China; ;3. Collaborative Innovation Center of IoT Industrialization and Intelligent Production, Minjiang University, Fuzhou 350108, China;
Shihong Xia, 2.Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; ;

Abstract

Abstract: The security of power operation and maintenance has always been a subject of great social concern. In order to avoid serious consequences caused by the fault of staffs, a motion recognition method for power operation and maintenance jobs based on LSTM (Long Short-Term Memory) is proposed, which covers the whole process from data collection, data processing to motion classification and recognition, then it can recognise and supervise the behavior of staffs who are at work. In addition, a simulation experiment is conducted between the deep learning algorithm LSTM and the traditional machine learning algorithm KNN based on the newly constructed data set of power operation and maintenance jobs. The results show that LSTM achieves better performance than KNN. When the time window is 120 frames, the accuracy based on LSTM reaches 91.32%, which is about 2 percentage points higher than KNN.

Recommended Citation

Liu, Peizhen; Jia, Yuxiang; and Xia, Shihong (2019) "An LSTM-based Motion Recognition Method for Power Operation and Maintenance," Journal of System Simulation: Vol. 31: Iss. 12, Article 33.
DOI: 10.16182/j.issn1004731x.joss.19-FZ0336
Available at: https://dc-china-simulation.researchcommons.org/journal/vol31/iss12/33

First Page

2837

Revised Date

2019-07-17

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.19-FZ0336

Last Page

2844

CLC

TP391.9

Recommended Citation

Liu Peizhen, Jia Yuxiang, Xia Shihong. An LSTM-based Motion Recognition Method for Power Operation and Maintenance[J]. Journal of System Simulation, 2019, 31(12): 2837-2844.

Corresponding Author

Yuxiang Jia,

DOI

10.16182/j.issn1004731x.joss.19-FZ0336