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在自动采矿卡车中的传感器故障检测,其总重量未知可变

Zengwei Li¹, Jun-Sheng Wang¹

¹College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110819, China.

ISA transactions

|January 31, 2025

概括

这项研究引入了一种新的方法,用于检测自动采矿卡车 (AMT) 中的传感器故障,尽管重量变化未知. 该方法通过改善状态估计和减少虚假报警,提高了恶劣的采矿环境中的安全性和可靠性.

科学领域:

工程工程师工程师工程师
机器人技术机器人技术机器人技术
控制系统控制系统

背景情况:

自主采矿卡车 (AMT) 在恶劣环境中运行,导致传感器退化.
传感器故障,如偏差和漂移,由于灰尘,极端温度和的道路而很常见.
重量传感器的不准确性对AMT运行和安全构成重大挑战.

研究的目的:

为AMTs开发一个强大的传感器故障检测系统.
为了应对国家估计中未知可变毛重的挑战.
为了减少传感器故障检测中的错误报警率.

主要方法:

使用区间观测器与zonotope方法进行状态估计,在未知的总重量变化下.
实施一个间隔残余生成器,结合概率比测试和探测故障的zonotope方法来检测故障.
模拟 AMT 模型上的拟议方法.

主要成果:

建议的间隔观察者有效地估计了AMT状态,尽管未知总重量变化.
间隔残余发生器成功检测到传感器故障,并降低了错误报警率.
模拟结果验证了开发的故障检测方法的有效性.

结论:

基于zonotope的间隔观察器和残余发生器为AMT中的传感器故障检测提供了可靠的解决方案.
这种方法提高了自动采矿设备的操作安全性和可靠性.
这种方法有效地减轻了环境因素和未知的车辆参数带来的挑战.

关键词:

自动采矿卡车自动采矿卡车故障检测检测故障检测间隔观察员是一个观察者.ゾノトープ法ゾノトープ法ゾノトープ法

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