Given that the main task of process monitoring (i.e., fault detection) is actually a classical one-class classification problem, the generalized regression neural network (GRNN) is directly inapplicable for handling process modeling and monitoring issues. Through the selection of only one variable to be the output while the others serve as the corresponding input, a GRNN model can then be constructed to approximate the nonlinear input to output relationship. The residuals, signifying the inconsistency between the actual measurement and the predicted output from the GRNN model, could be a good indicator for online fault detection. The proposed nonlinear process monitoring approach is termed decentralized GRNN (DGRNN), which applies the GRNN in an extremely decentralized manner and utilizes the squared Mahalanobis distance for the online monitoring of the abnormalities captured by the generated residuals. The effectiveness and superiority of the DGRNN-based nonlinear process monitoring approach over other state-of-the-art nonlinear process monitoring methods are investigated by comparisons in two nonlinear processes.

鉴于过程监控的主要任务（即，故障检测）实际上是经典的一类分类问题，广义回归神经网络（GRNN）直接不适用于处理过程建模和监视问题。通过选择仅一个变量作为输出，而其他变量作为对应的输入，则可以构建GRNN模型以近似非线性输入到输出的关系。残差表明GRNN模型的实际测量值与预测输出之间存在不一致，可能是在线故障检测的良好指标。提出的非线性过程监控方法称为分散式GRNN（DGRNN），它以极为分散的方式应用GRNN，并利用平方的Mahalanobis距离对生成的残差捕获的异常进行在线监视。通过在两个非线性过程中进行比较，研究了基于DGRNN的非线性过程监视方法相对于其他最新的非线性过程监视方法的有效性和优越性。