矿山通风系统智能故障诊断的一类分类模型
Wen Luo1, Youxin Zhao2,3,4
1China Energy Shendong Coal Group Co., Ltd, Yulin, 719315, Shanxi, China. m13322353907@163.com.
Scientific reports
|November 6, 2024
概括
一个新的MC-OCSVM模型使用风速数据准确地识别了采矿通风系统中的断层分支. 这种强大的算法实现了高精度和快速诊断时间,提高了矿山的安全性.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 数据挖掘 数据挖掘
背景情况:
- 矿山通风系统对于安全至关重要,但故障检测仍然具有挑战性.
- 现有的方法可能难以处理复杂的故障模式和实时诊断.
- 准确的故障分支识别对于防止危险情况至关重要.
研究的目的:
- 为矿山通风系统开发一个有效的故障诊断模型.
- 为了解决当前故障识别技术的局限性.
- 利用矿山监测风速数据进行增强诊断.
主要方法:
- 引入一类分类算法来构建MC-OCSVM模型.
- 集成多个一个类支持向量机器 (OCSVMs) 具有统一的超参数.
- 将故障诊断转化为数据利用的最大决策距离问题.
主要成果:
- 在KEEL数据集上的实验验证证证了该模型的多分类能力.
- 与其他一类分类整合模型相比,MC-OCSVM模型表现出优越的概括性.
- 在Buertai煤矿的实地实验实现了93.2%的准确性,诊断时间为1.2秒.
结论:
- 拟议的MC-OCSVM模型有效地解决了矿山通风系统故障诊断问题.
- 该算法提供快速,准确和强大的故障分支识别.
- 这种方法提高了矿山通风操作的可靠性和安全性.
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