Quality-relevant fault detection aims to reveal whether quality variables are affected when a fault is detected. For current industrial processes, kernel-based methods focus on the nonlinearity within process variables, which is insufficient for obtaining nonlinearities of quality variables. Alternatively, neural network is an option for nonlinear prediction. However, these models are driven by predictive errors on samples. For quality-relevant tasks, the key is to capture the trends of quality variables. Therefore, this study proposes a new model, namely, maximizing correlation neural network (MCNN), to predict the quality-relevant information intuitively. The MCNN is trained to maximize the linear correlation between quality variables and the combinations of nonlinear representations mapped by a multilayer feedforward network. As such, fault detection can be implemented in the quality-relevant and irrelevant subspaces on the basis of the deep most correlated representations of process variables. Considering that different variables have different sensitivities to quality at various locations due to their nonlinear relationship, fault backpropagation is designed in the MCNN to isolate the faulty variables on the basis of real-time faulty information. Finally, numerical example and Tennessee Eastman process are used to evaluate the proposed method, which exhibits a competitive performance.

与质量相关的故障检测旨在揭示检测到故障时质量变量是否受到影响。对于当前的工业过程，基于核的方法关注过程变量内的非线性，这不足以获取质量变量的非线性。另外，神经网络是非线性预测的一种选择。但是，这些模型是由样本上的预测误差驱动的。对于与质量相关的任务，关键是捕获质量变量的趋势。因此，本研究提出了一种新模型，即最大化相关神经网络（MCNN），以直观地预测与质量相关的信息。对MCNN进行了训练，以最大程度地提高质量变量与多层前馈网络映射的非线性表示的组合之间的线性相关性。因此，可以基于过程变量的最深层关联表示，在质量相关和无关的子空间中实现故障检测。考虑到不同的变量由于其非线性关系而在不同位置对质量的敏感性不同，因此在MCNN中设计了故障反向传播算法，以基于实时故障信息隔离故障变量。最后，通过数值算例和田纳西·伊士曼过程对所提出的方法进行了评估，该方法具有很好的竞争力。MCNN中设计了故障反向传播，以基于实时故障信息隔离故障变量。最后，通过数值算例和田纳西·伊士曼过程对所提出的方法进行了评估，该方法具有很好的竞争力。MCNN中设计了故障反向传播，以基于实时故障信息隔离故障变量。最后，通过数值算例和田纳西·伊士曼过程对所提出的方法进行了评估，该方法具有很好的竞争力。