数据驱动方法在化工过程故障诊断中的研究进展

doi:10.16085/j.issn.1000-6613.2020-2007

摘要/Abstract

摘要：

数据驱动方法是一种黑箱模型，具有自主挖掘和构建数据内在关系的优点。随着感知设备的发展和计算能力的提升，数据驱动方法在化工过程故障诊断的研究领域体现出了更大的优势。本文介绍了各类数据驱动方法的原理和作用，并分析了其各自的优缺点与实际应用方向，总结得出深度学习和集成学习是数据驱动方法未来研究重点。同时，本文回顾了近五年来国内外数据驱动方法在化工过程故障诊断中的研究与应用，综合分析了现阶段该领域的研究情况，表明将多种数据驱动方法进行组合来解决化工过程问题的思路具有一定的有效性。并进一步给出了关于数据异常、时间滞后等问题的研究方向。最后，本文建议更多地从方法的机理出发对方法进行研究和优化，在未来的研究思考中应更着重于实用性和时效性。

关键词: 故障诊断, 数据驱动方法, 化工过程, 特征提取, 时间序列

Abstract:

The data-driven method is a black-box model, which has the advantage of autonomously mining and constructing the internal relationship of data. The development of perception equipment and the improvement of computing power highlight the advantages of the data-driven method, which can better independently mine and build the internal relationship of data. This paper introduces the principles and functions of various data-driven methods, analyzes the advantages and disadvantages of the methods and the practical application direction, and draws the conclusion that deep learning and integrated learning are the key research points of data-driven methods in the future. This paper reviews the research and application of data-driven methods for fault diagnosis in chemical processes in the recent five years, and finally comprehensively analyzes the current research situation in this field. The analysis shows that it is effective to solve problems in chemical processes by combining multiple data-driven methods. Furthermore, the research direction of data anomaly and time lag is provided. In this paper, it suggested that the mechanism of the method should be studied and optimized. In the future, the development and research of data-driven methods for fault diagnosis in chemical processes should focus on two points, namely “practicality” and “timeliness”.

Key words: fault detection and diagnosis, data-driven method, chemical process, feature extraction, time series

中图分类号:

TQ086.3

姚羽曼, 罗文嘉, 戴一阳. 数据驱动方法在化工过程故障诊断中的研究进展[J]. 化工进展, 2021, 40(4): 1755-1764.

YAO Yuman, LUO Wenjia, DAI Yiyang. Research progress of data-driven methods in fault diagnosis of chemical process[J]. Chemical Industry and Engineering Progress, 2021, 40(4): 1755-1764.

图/表 3

参考文献 58

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名称	高斯分布	优点	缺点	应用
PCA	是	大规模稳态数据的降维效果好^[9]	受数据污染遮挡影响大	适用于数据降维
PLS	是	PCA、相关性分析、多元线性回归一体^[8]	故障分离不彻底	适用于需要预测不可测变量的数据
ICA	否	能获得特征相互独立的特征集^[10]	需要数据维度中至多一个维度符合高斯分布	适用于特征提取
GMM	否	能处理多模态数据^[4,6]	易过度拟合，易受噪声影响	适用于混合分布的大样本聚类

名称	高斯分布	优点	缺点	应用
PCA	是	大规模稳态数据的降维效果好^[9]	受数据污染遮挡影响大	适用于数据降维
PLS	是	PCA、相关性分析、多元线性回归一体^[8]	故障分离不彻底	适用于需要预测不可测变量的数据
ICA	否	能获得特征相互独立的特征集^[10]	需要数据维度中至多一个维度符合高斯分布	适用于特征提取
GMM	否	能处理多模态数据^[4,6]	易过度拟合，易受噪声影响	适用于混合分布的大样本聚类

类型	名称	优点	缺点	应用
决策树	ID3 C4.5^[19] CART	简单，运算快；适用于连续和离散数据；自由学习任何形式的映射^[20]；模型解释性强	泛化能力弱；过拟合；结果偏向多数类；易受数据不平衡影响	属性混合数据的分类^[15]
人工神经网络	BP RBF	联想记忆；抗干扰能力强	易陷入局部极小值	数据量较小的回归问题
深度学习	SAE CNN DBN RNN	提取局部特征；多源信息处理能力；特征提取能力；推测和补全信息的能力	信息缺失问题；易局部最优；运算时间长；梯度爆炸	数据量较大的强非线性过程
支持向量机	SVM	不易陷入局部极小	对数据缺失和核函数的选择敏感	数据量小的高维非线性分类问题
集成学习	bagging boosting stacking	良好的抗噪能力；减小数据偏置；泛化能力强	可能缺失对重要样本的训练；对样本噪声敏感；计算复杂度高；计算时间长	数据维度高、结构复杂、特征模糊过程^[18]

类型	名称	优点	缺点	应用
决策树	ID3 C4.5^[19] CART	简单，运算快；适用于连续和离散数据；自由学习任何形式的映射^[20]；模型解释性强	泛化能力弱；过拟合；结果偏向多数类；易受数据不平衡影响	属性混合数据的分类^[15]
人工神经网络	BP RBF	联想记忆；抗干扰能力强	易陷入局部极小值	数据量较小的回归问题
深度学习	SAE CNN DBN RNN	提取局部特征；多源信息处理能力；特征提取能力；推测和补全信息的能力	信息缺失问题；易局部最优；运算时间长；梯度爆炸	数据量较大的强非线性过程
支持向量机	SVM	不易陷入局部极小	对数据缺失和核函数的选择敏感	数据量小的高维非线性分类问题
集成学习	bagging boosting stacking	良好的抗噪能力；减小数据偏置；泛化能力强	可能缺失对重要样本的训练；对样本噪声敏感；计算复杂度高；计算时间长	数据维度高、结构复杂、特征模糊过程^[18]

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