基于动态特征提取的工业过程故障分类和诊断的二次差异分析
Hanqi Li1, Mingxing Jia1,2, Zhizhong Mao1,2
1College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.
Entropy (Basel, Switzerland)
|December 23, 2023
概括
本研究提出了一种使用动态特征提取和重建错误的动态过程中故障分类的新方法. 该方法改善了工业监控系统的早期故障诊断.
科学领域:
- 工业过程监控 工业过程监控
- 错误诊断 错误诊断 在
- 非线性动力学是一种非线性动力学.
背景情况:
- 动态非线性过程对故障分类具有挑战,原因是高维度和有限的样本.
- 准确和早期的故障诊断对于可靠的工业运作和防止故障至关重要.
研究的目的:
- 引入一种新的方法来提高动态非线性过程中的故障分类和诊断.
- 为了解决高维和样本有限的故障分类问题的局限性.
- 为了能够在线样本中早期诊断出故障,即使是那些幅度小于训练数据的样本.
主要方法:
- 从多变量时间序列数据中提取动态特征.
- 使用动态重建错误来增强功能集.
- 使用加权最大散射差 (WMSD) 来减少维度.
- 使用二次差异分析 (QDA) 进行故障分类.
主要成果:
- 与LDA和KFD等传统方法相比,提出的方法在故障分类和诊断方面表现优越.
- 有效处理高维,样本有限的故障分类场景.
- 在冷模拟模型中成功早期诊断故障,包括低振幅在线样本.
结论:
- 这种新方法显著提高了动态非线性过程中的故障分类和诊断.
- 该方法提供可靠的工业过程监控和早期故障检测能力.
- 该技术为复杂的工业故障分析提供了强大的解决方案.
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