In this paper, in order to monitor the slow-time-varying industrial process, an adaptive method is proposed based on the neural network model and fault reconstruction method. Firstly, a unified neural network algorithm is introduced to extract the principal and minor eigen subspace with low computational complexity, and the whole eigenspace is divided into three partitions to further reduce the complexity of high-dimensional data computation. Then, the process is monitored based on a combined statistic index and the corresponding adaptive threshold. Moreover, the eigen subspace can still be updated even when in a faulty case. Finally, computer simulation confirms the capacity of the proposed method for high-dimensional, slow-time-varying process monitoring.

本文针对慢时变工业过程的监控，提出了一种基于神经网络模型和故障重构方法的自适应方法。首先，引入统一的神经网络算法，提取计算复杂度较低的主次特征子空间，将整个特征空间分为三个部分，进一步降低高维数据计算的复杂度。然后，基于组合的统计指标和相应的自适应阈值来监控该过程。此外，即使在故障情况下，特征子空间仍然可以更新。最后，计算机仿真证实了所提出的方法对于高维、慢时变过程监控的能力。