基于随机负载条件的比例电磁的热负荷模型
Chenyu Liu1, Anlin Wang1, Xiaotian Li1
1School of Mechanical Engineering, Tongji University, Shanghai 201804, China.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
本研究引入了一种基于卡尔曼波器的模型,用于在随机条件下预测比例电磁的热负荷. 该模型通过提高热负载预测准确性和适应性来提高可靠性.
科学领域:
- 电子机械系统工程 电子机械系统工程
- 可靠性工程可靠性工程
- 热管理 热管理
背景情况:
- 由于热效应,电机系统中的随机负载变化给比例电磁带来了可靠性挑战.
- 准确的热负荷预测对于在线监测,异常检测和数据归算至关重要.
研究的目的:
- 开发和验证在随机负载条件下对比例电磁的热负载预测模型.
- 提高机电系统的可靠性和操作监控.
主要方法:
- 利用卡尔曼波器算法进行热负荷预测.
- 使用k-means集群来分析挖掘机运行的历史功率和热负荷数据.
- 应用Grubbs标准用于异常值的检测和公正的估计.
主要成果:
- 与其他方法相比,提出的卡尔曼波器模型显示出更高的预测准确性.
- 该模型表现出强大的适应性,最大预测偏差在5%以内.
- 交叉验证证实了该模型在特定热负荷特征方面的有效性.
结论:
- 基于卡尔曼波器的热负荷预测模型有效地解决了随机负载下的比例电磁的可靠性问题.
- 这种方法为分析低频电机产品中的随机循环热负荷提供了有价值的参考.
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