Abstract:

An integrated fault diagnosis method based on independent component analysis (ICA) and support vector machines (SVM) is proposed to resolve the problems of the difficulty in fault diagnosis for complex operation and multi-loop controls of chemical industry process.The basic idea of the proposed diagnosis method is to use ICA arithmetic to extract the essential independent components.And, I²，I_e² and SPE charts are proposed as on-line fault detecting strategy.The contribution chart of every monitoring variable to I²，I_e² and SPE are calculated separately using the gradient algorithm, and used to extract the preliminary possible fault resource by monitoring the change of contributions.Finally, faults are diagnosed further from possible fault resource using binary tree SVM.The proposed fault diagnosis method is proved to be effective by simulation with the data from a real fault in an industrial butadiene distillation column.

Key words:

独立成分分析, 支持向量机, 梯度算法, 丁二烯精馏装置

摘要：

针对由于复杂操作或多回路控制等因素造成复杂化工过程故障诊断难度加剧问题，提出了一种基于独立成分分析（ICA）和支持向量机（SVM）的集成故障诊断方法。该方法利用快速ICA算法建立正常工况ICA模型，通过监控统计量I²、I_e²、SPE是否超过用核密度估计方法确定相应的置信限检测故障。如检测到故障发生，即用梯度算法计算每一个监控变量对统计量I²、I_e²、SPE的贡献度，根据观察贡献度变化情况初步诊断出可能的故障源，并利用支持向量机多分类算法诊断出初始故障源。利用丁二烯精馏装置的实际工业故障数据验证提出的ICA-SVM集成故障诊断方法的有效性。

关键词:

独立成分分析, 支持向量机, 梯度算法, 丁二烯精馏装置