LSTM-Based Analysis of Industrial IoT Equipment
|Author:||Zhang, Weishan1; Guo, Wuwu1; Liu, Xin1;|
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
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2018081433662
Institute of Electrical and Electronics Engineers,
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.
|Pages:||23551 - 23560|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
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.
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