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Abstract <p>Remote data monitoring of hydraulic pump stations holds significant value for fault diagnosis and prediction. This paper presents a monitoring system for hydraulic pump stations utilizing an Internet of Things (IoT) module. The system employs the ESP8266 IoT data module and utilizes the STM32F767VGT6 microcontroller as the primary controller, facilitating data transmission and exchange through wireless communication. The monitoring client system is developed on the Alibaba Cloud IoT Studio platform, enabling connection to the cloud server for data monitoring and command transmission. Experimental validation of the system demonstrates stable operation, remote data collection, and cloud storage capabilities. Additionally, the testing errors for pressure, oil temperature, oil level, and flow rate remain below 0.6%, indicating high measurement accuracy. This design offers valuable options for the fault diagnosis and prediction of hydraulic pump stations.</p>

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Design of Monitoring and Control System of Hydraulic Pump Station Based on Internet of Things

  • 期刊论文

  • 发表日期：

    2025年6月1日

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  • Ping Xu,
  • Shuangfei Zhang

摘要

Abstract

Remote data monitoring of hydraulic pump stations holds significant value for fault diagnosis and prediction. This paper presents a monitoring system for hydraulic pump stations utilizing an Internet of Things (IoT) module. The system employs the ESP8266 IoT data module and utilizes the STM32F767VGT6 microcontroller as the primary controller, facilitating data transmission and exchange through wireless communication. The monitoring client system is developed on the Alibaba Cloud IoT Studio platform, enabling connection to the cloud server for data monitoring and command transmission. Experimental validation of the system demonstrates stable operation, remote data collection, and cloud storage capabilities. Additionally, the testing errors for pressure, oil temperature, oil level, and flow rate remain below 0.6%, indicating high measurement accuracy. This design offers valuable options for the fault diagnosis and prediction of hydraulic pump stations.