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Open AccessArticle

The Temperature Forecast of Ship Propulsion Devices from Sensor Data

by Taoying Li 1,2,*, Miao Hua 1 and Qian Yin 1
1
School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China
2
Logistics Research Institute, Dalian Maritime University, Dalian 116026, China
*
Author to whom correspondence should be addressed.
Information 2019, 10(10), 316; https://doi.org/10.3390/info10100316
Received: 5 September 2019 / Revised: 9 October 2019 / Accepted: 14 October 2019 / Published: 16 October 2019
The big data from various sensors installed on-board for monitoring the status of ship devices is very critical for improving the efficiency and safety of ship operations and reducing the cost of operation and maintenance. However, how to utilize these data is a key issue. The temperature change of the ship propulsion devices can often reflect whether the devices are faulty or not. Therefore, this paper aims to forecast the temperature of the ship propulsion devices by data-driven methods, where potential faults can be further identified automatically. The proposed forecasting process is composed of preprocessing, feature selection, and prediction, including an autoregressive distributed lag time series model (ARDL), stepwise regression (SR) model, neural network (NN) model, and deep neural network (DNN) model. Finally, the proposed forecasting process is applied on a naval ship, and the results show that the ARDL model has higher accuracy than the three other models. View Full-Text
Keywords: ship propulsion devices; forecast; feature selection; ARDL ship propulsion devices; forecast; feature selection; ARDL
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Li, T.; Hua, M.; Yin, Q. The Temperature Forecast of Ship Propulsion Devices from Sensor Data. Information 2019, 10, 316.

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