一个自主开发的火灾预警系统,基于气体检测和图形卷积计算方法
Yanwei Wang1,2, Yang Yu1,2,3, Boxu Zhou1,3
1School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China.
ACS sensors
|May 14, 2025
概括
这项研究引入了一种人工嗅觉系统,用于在电气柜中早期检测火灾风险. 该系统准确地识别过热材料的异常气味,证明其可用于增强安全监控的可行性.
科学领域:
- 电气工程 电气工程
- 化学传感器 化学传感器
- 人工智能的人工智能
背景情况:
- 传统的火灾检测方法 (温度,烟雾,声音,电流) 在电气柜等复杂环境中存在局限性.
- 在电气柜中早期发现火灾危险对于防止灾难性故障至关重要.
- 异常气味检测提供了一种新的方法,独立于环境复杂性和电气条件.
研究的目的:
- 开发一种人工嗅觉系统,用于在电气柜中早期监测火灾风险.
- 用图形卷积网络评估一种新型人工嗅觉训练装置的性能.
- 为了证明从过热材料中检测挥发性气体的可行性.
主要方法:
- 开发一个带有感官数据收集器的人工嗅觉训练装置.
- 在无烟,可控加热条件下从六种可燃材料收集气味数据.
- 快速Pearson图形卷积网络 (FPGCN) 的应用用于挥发性气体的识别.
主要成果:
- 来自不同过热材料的挥发性气体的高性能识别.
- 在1-350秒内实现了98.08%的准确性,98.21%的精度,98.01%的回忆.
- 证明了系统在识别火灾风险的异常气味方面的有效性.
结论:
- 人工嗅觉系统是电气柜中早期火灾风险监测的可行和有效方法.
- 开发的FPGCN模型在识别特定的挥发性气体方面表现出很高的准确性.
- 这项技术为传统的火灾检测系统提供了一个有希望的替代方案.
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