Energy Efficiency Optimization of Industrial Internet of Things and Predictive Maintenance of Equipment

Yilin Ouyang

doi:10.47297/taposatWSP2633-456921.20250609

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Energy Efficiency Optimization of Industrial Internet of Things and Predictive Maintenance of Equipment

6卷第9期

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Energy Efficiency Optimization of Industrial Internet of Things and Predictive Maintenance of Equipment
Energy Efficiency Optimization of Industrial Internet of Things and Predictive Maintenance of Equipment
School of Mechanical and Electrical Engineering and Automation, Shanghai University, Shanghai, 200444, China
2025年6卷第9期页码：102-106
纸质出版：2025-09-30
DOI：10.47297/taposatWSP2633-456921.20250609
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Yilin Ouyang. (2025). Energy Efficiency Optimization of Industrial Internet of Things and Predictive Maintenance of Equipment. 科技理论与实践（英文版）, 6(9), 102-106.

Ouyang Yilin.Energy Efficiency Optimization of Industrial Internet of Things and Predictive Maintenance of Equipment[J].Theory and Practice of Science and Technology,2025,06(09):102-106.
Yilin Ouyang. (2025). Energy Efficiency Optimization of Industrial Internet of Things and Predictive Maintenance of Equipment. 科技理论与实践（英文版）, 6(9), 102-106. DOI： 10.47297/taposatWSP2633-456921.20250609.

Ouyang Yilin.Energy Efficiency Optimization of Industrial Internet of Things and Predictive Maintenance of Equipment[J].Theory and Practice of Science and Technology,2025,06(09):102-106. DOI： 10.47297/taposatWSP2633-456921.20250609.

摘要

Abstract

This study aims to explore the key paths for energy efficiency optimization and predictive maintenance of equipment in the industrial Internet of Things (IoT) environment. In response to the challenges such as low utilization of on-site industrial data and the difficulty in balancing the real-time performance and economic efficiency of algorithms

modern industries mainly adopt solutions including association rule algorithms and neural network algorithms

and construct a collaborative architecture of "edge real-time perception + cloud global optimization". Meanwhile

to verify this method

the author designed a simulation scheme: taking the data from an engine factory as the object

the Apriori algorithm was used to mine the association rules of parameters such as temperature

humidity

and power

and a BP neural network dynamic prediction model was constructed. The parameters were optimized through gradient descent

and the effectiveness of the model in reducing unplanned downtime and energy consumption was ultimately evaluated

providing empirical evidence for industrial applications.

关键词

Keywords

references

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