基于稀疏距离的间歇过程故障检测方法

Fault-Detection Method for Batch Process Based on Sparse Distance

针对间歇过程多工序、变量非线性、非高斯分布等特征,提出了一种基于稀疏距离的故障检测方法(FD-SD).采用稀疏距离衡量测试样本周围训练样本的分布密度,统计测试样本近距离训练样本分布特征,应用变窗宽核密度估计方法估算样本距离的累计分布函数,根据阈值计算样本的稀疏距离.根据稀疏距离的累计分布函数设定检测控制限,建立基于稀疏距离的检测模型.该方法可以避免变量服从高斯、线性分布等假设问题,同时使故障检测的准确性与可靠性得到提高.通过在模拟实例和半导体蚀刻批次过程中的仿真应用,说明该方法可以处理过程具有非线性、多模态、多工序生产特征的故障检测问题.仿真实验验证了方法的有效性.

Aiming at the features of multiple product processes, nonlinear and non-Gaussian, a fault detection method in batch process based on sparse distance （FD-SD） is proposed. Sparse distance is used to measure the density of training samples around a test sample and to analyze the training samples distribution feature near the test sample. Sparse distance is calculated by cumulative distribution function of sample distance through kernel density estimate function with changed window width. The control limit is calculated by cumulative distribution function of sparse distance, and then the detection model based on sparse distance is built. FD-SD does not needs the hypothesis of variables obeying Gauss and linear distribution and can improve the accuracy and reliability of the fault detection process. From Simulation results in artificial case and semicon- ductor etch batch process, it is shown that FD-SD can detect fault in nonlinear and muhi-mode processes. The validity of FD-SD is proved by the results.