动态化学过程的故障诊断基于改进的残余网络与隔门循环单元相结合
Shiqian Han1,2, Pingping Wang3,2, Cheng Zhang1,2
1College of Science, Shenyang University of Chemical Technology, Shenyang, Liaoning 110142, China.
ACS omega
|March 17, 2025
概括
本研究介绍了一种IResNet-GRU模型,用于动态化学过程中准确的故障诊断. 它通过评估特征贡献,有效地识别根源变量,优于传统方法.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 过程控制 过程控制
- 数据分析数据分析数据分析.
背景情况:
- 在动态化学过程数据中区分变量贡献对于故障诊断具有挑战性.
- 现有的方法可能无法有效地确定根源变量或捕捉复杂的时间动态.
研究的目的:
- 提出一种使用IResNet-GRU模型用于动态化学过程的新型故障诊断方法.
- 通过识别根源原因变量来提高故障诊断的解释性.
- 在复杂的工业环境中提高故障诊断的准确性和有效性.
主要方法:
- 使用主要组件分析 (PCA) 来计算对应矩阵,以输入到注意力模块.
- 开发了一个改进的剩余网络 (IResNet),包含一个注意模块来权衡提取的特征并评估变量的意义.
- 将增强的原始数据转化为2D格式,使用滑动窗技术捕获时空特征.
- 集成了一个Gated Recurrent Unit (GRU) 来有效地从增强数据中提取动态特征.
主要成果:
- IResNet-GRU模型成功地确定了导致过程故障的根源变量.
- 注意力机制有效地赋予特征权重,区分监控变量的重要性.
- 与传统故障诊断技术相比,该方法在田纳西-伊斯特曼化学工艺数据集上表现出优异的性能.
结论:
- 拟议的IResNet-GRU模型为动态化学过程中的故障诊断提供了强大的和可解释的方法.
- 注意力机制和GRU的整合增强了识别关键变量和捕获时间依赖性的能力.
- 该方法为确保化学操作的安全性和效率提供了显著的进步.
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