检测非线性动态工厂乱的方法
Xuechun Wang1, Vladimir Eliseev1,2
1Department of Control and Intelligent Technologies, National Research University "Moscow Power Engineering Institute", Krasnokazarmennaya 14, Moscow 111250, Russia.
Sensors (Basel, Switzerland)
|February 26, 2025
概括
一种新的疾病检测方法,CCF-AE,使用输入输出数据和神经网络自编码器. 这种方法为动态工厂过程监控提供了更高的准确性和更少的错误报警.
科学领域:
- 过程控制和监测过程控制和监测.
- 机器学习在工程中的应用.
- 动态系统分析 动态系统分析
背景情况:
- 准确的障碍检测对于保持动态工业过程的稳定性和效率至关重要.
- 像CUSUM和EWMV这样的传统方法可能会与复杂的非线性系统作斗争.
- 对于实时监控而言,无模型方法是可取的,而不需要参考模型.
研究的目的:
- 引入一种名为CCF-AE.的新型,无模型的疾病检测方法.
- 在非线性过程中对CCF-AE的性能与已建立的方法进行评估.
- 为了证明CCF-AE对复杂动态系统的有效性.
主要方法:
- 开发了使用交叉相关函数和神经网络自编码器的CCF-AE方法.
- 应用CCF-AE来监测非线性pH中和反应过程.
- 将CCF-AE性能与累积和 (CUSUM) 和指数加权移动变量 (EWMV) 控制图进行比较.
主要成果:
- 与CUSUM和EWMV相比,CCF-AE实现了更高的真实检测率.
- 拟议的CCF-AE方法表现出明显较低的错误报警率.
- CCF-AE在复杂的非线性过程中检测障碍的增强能力.
结论:
- CCF-AE是一种有效的无模型方法,用于在动态植物中检测障碍.
- 该方法在非线性系统的传统控制图表上显示了显著的优势.
- CCF-AE为实时过程监控和异常检测提供了强大的解决方案.
相关概念视频
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Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
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01:26Linear Approximation in Frequency Domain
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01:18Second Order systems II
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01:19Root-Locus Method
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01:26PD Controller: Design
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01:07Time-Domain Interpretation of PD Control
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