Research on Remote Monitoring and Fault Diagnosis of All-Weather Strolling House for Horses outside Stables

In this paper, the remote monitoring and fault diagnosis technology of all-weather stables outside horses is deeply studied, and a set of intelligent management system integrating remote monitoring, fault diagnosis, sterilization and harmless treatment is proposed. The system uses the Internet of Things technology to collect horse activity data, environmental parameters, and equipment status information in the walking room in real time, and processes the collected horse images through the rotation normalization algorithm of horse body size measurement to accurately measure the horse size and ensure the accuracy of the data. In terms of fault diagnosis, this paper designs a fault prediction model based on deep learning, which can automatically identify and predict the potential faults of the equipment in the walking room. Through model simulation, its high accuracy and low false positive rate in fault detection are verified. In addition, the system also has automatic sterilization and harmless treatment functions, which can automatically start sterilization procedures according to environmental monitoring data, effectively prevent the spread of diseases and ensure the health of horses. The experimental results show that the system can effectively improve the management efficiency of the all-weather stables outside the stables, reduce manual intervention, improve the welfare of horses, and reduce the equipment failure rate and maintenance cost. Through remote monitoring and intelligent diagnosis, the system provides strong technical support for horse health management and opens a new path for intelligent management of the horse industry.

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Publication Details

Published
2024-12-29
DOI
https://doi.org/10.1109/iceace63551.2024.10898792
Primary Topic
Remote Sensing and Land Use
Type
article
Field-Weighted Citation Impact
0.00

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article

Research on Remote Monitoring and Fault Diagnosis of All-Weather Strolling House for Horses outside Stables

Sun Zhuo, Xin Yan
Remote Sensing and Land Use
article

Research on Remote Monitoring and Fault Diagnosis of All-Weather Strolling House for Horses outside Stables

Sun Zhuo, Xin Yan
article en

Abstract

In this paper, the remote monitoring and fault diagnosis technology of all-weather stables outside horses is deeply studied, and a set of intelligent management system integrating remote monitoring, fault diagnosis, sterilization and harmless treatment is proposed. The system uses the Internet of Things technology to collect horse activity data, environmental parameters, and equipment status information in the walking room in real time, and processes the collected horse images through the rotation normalization algorithm of horse body size measurement to accurately measure the horse size and ensure the accuracy of the data. In terms of fault diagnosis, this paper designs a fault prediction model based on deep learning, which can automatically identify and predict the potential faults of the equipment in the walking room. Through model simulation, its high accuracy and low false positive rate in fault detection are verified. In addition, the system also has automatic sterilization and harmless treatment functions, which can automatically start sterilization procedures according to environmental monitoring data, effectively prevent the spread of diseases and ensure the health of horses. The experimental results show that the system can effectively improve the management efficiency of the all-weather stables outside the stables, reduce manual intervention, improve the welfare of horses, and reduce the equipment failure rate and maintenance cost. Through remote monitoring and intelligent diagnosis, the system provides strong technical support for horse health management and opens a new path for intelligent management of the horse industry.

Wuhan Business University (CN)
Wuhan Business University
Sustainable cities and communities
Openalex Percentile: Top 71%
Remote Sensing and Land Use
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