University of Oulu

W. Zhang et al., "LSTM-Based Analysis of Industrial IoT Equipment," in IEEE Access, vol. 6, pp. 23551-23560, 2018. doi: 10.1109/ACCESS.2018.2825538

LSTM-Based Analysis of Industrial IoT Equipment

Saved in:
Author: Zhang, Weishan1; Guo, Wuwu1; Liu, Xin1;
Organizations: 1Department of Software Engineering, China University of Petroleum
2Faculty of Engineering and Computer Science, Concordia University
3Faculty of Information Technology and Electrical Engineering, University of Oulu
4College of Computer Science and Technology, Fudan University
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 8.2 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2018
Publish Date: 2018-08-14


Industrial Internet of Things (IIoT) is producing massive data which are valuable for knowing running status of the underlying equipment. However, these data involve various operation events that span some time, which raise questions on how to model long memory of states, and how to predict the running status based on historical data accurately. This paper aims to develop a method of: (1) analyzing equipment working condition based on the sensed data; (2) building a prediction model for working status forecasting and designing a deep neural network model to predict equipment running data; and (3) improving the prediction accuracy by systematic feature engineering and optimal hyperparameter searching. We evaluate our method with real-world monitoring data collected from 33 sensors of a main pump in a power station for three months. The model achieves less root mean square error than that of autoregressive integrated moving average model. Our method is applicable to general IIoT equipment for analyzing time series data and forecasting operation status.

see all

Series: IEEE access
ISSN: 2169-3536
ISSN-E: 2169-3536
ISSN-L: 2169-3536
Volume: 6
Pages: 23551 - 23560
DOI: 10.1109/ACCESS.2018.2825538
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by the Key Research Program of Shandong Province under Grant 2017GGX10140, in part by the Fundamental Research Funds for the Central Universities under Grant 15CX08015A, and in part by the National Natural Science Foundation of China under Grant 61309024.
Copyright information: © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See for more information.