In advanced industrial fields such as chemical processes, faults must absolutely be detected; we cannot afford to operate with failing operative parts. It is therefore, necessary to take into consideration the uncertainties in order to establish a guaranteed failure detection. For this propose, several approaches have been introduced in the literature based on the Principal Components Analysis (PCA) approach for interval-valued data. However, these developed approaches treated only the linear process models. To overcome this drawback, recently, the researchers proposed to extend monitoring process based on Kernel Principal Components Analysis (KPCA) approach to the nonlinear uncertain case, in which two models are developed: model based on the lower bound (LB) and upper bound (UB) and model based on midpoints and radii.

Nevertheless, the number of variables that adequately represent the normal operating condition of system can be very large. To deal with both high calculation costs and the memory storage, in this paper, we suggest an improved kernel method, in order to ameliorate failure detection process for nonlinear uncertain system. The suggested method, fuse the benefit of Interval Kernel Generalized Likelihood Ratio Test (IKGLRT) index with the Exponentially Weighted Moving Average (EWMA) filter and the Interval Reduced Kernel Principal Component (IRR-KPCA).

The failure detection performance of the suggested method is valued using a Tennessee Eastman Process (TEP).

在化学工业等先进工业领域，必须绝对地检测出故障。我们不能承担因零件故障而造成的损失。因此，有必要考虑不确定性以建立有保证的故障检测。为此，在文献中基于间隔值数据的主成分分析（PCA）方法引入了几种方法。但是，这些已开发的方法仅处理线性过程模型。为了克服这一缺陷，最近，研究人员提议将基于核主成分分析（KPCA）方法的监视过程扩展到非线性不确定情况，其中开发了两种模型：基于下限（LB）和上限（ UB）和基于中点和半径的模型。

但是，足以代表系统正常运行状况的变量数量可能非常大。为了解决高计算量和内存存储的问题，本文提出了一种改进的核方法，以改善非线性不确定系统的故障检测过程。建议的方法将“间隔内核广义似然比测试（IKGLRT）”的好处与“指数加权移动平均值”（EWMA）过滤器和“间隔减少的内核主成分”（IRR-KPCA）融合在一起。

建议的方法的故障检测性能使用田纳西州伊士曼过程（TEP）进行评估。