基于多模型方法的化工过程鲁棒故障诊断

R. Patton, C. J. Lopez-Toribio, S. Simani
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引用次数: 13

摘要

提出了一种基于鲁棒模型的化工过程传感器故障检测与隔离技术。该诊断系统基于对过程输出的鲁棒估计。利用Takagi-Sugeno (T-S)多模型模糊辨识方法,得到了所研究过程的动态非线性模型。结合识别和残差生成方案对建模不确定性、干扰和测量噪声具有鲁棒性,为故障检测和故障隔离提供了良好的灵敏度。所识别的系统由T-S模型的模糊组合组成,用于检测工厂运行条件的变化。残差分析和几何测试分别足以进行故障检测和隔离。将该方法应用于罐式反应器化工过程基准仿真中的故障检测与隔离问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust fault diagnosis in a chemical process using multiple-model approach
Presents a robust model-based technique for the detection and isolation of sensor faults in a chemical process. The diagnosis system is based on the robust estimation of process outputs. A dynamic non-linear model of the process under investigation is obtained by a procedure exploiting Takagi-Sugeno (T-S) multiple-model fuzzy identification. The combined identification and residual generation schemes have robustness properties with respect to modelling uncertainty, disturbance and measurement noise, providing good sensitivity properties for fault detection and fault isolation. The identified system consists of a fuzzy combination of T-S models to detect changing plant operating conditions. Residual analysis and geometrical tests are then sufficient for fault detection and isolation, respectively. The procedure presented is applied to the problem of detecting and isolating faults in a benchmark simulation of a tank reactor chemical process.
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