High-dimensional, slow-time-varying process monitoring technique based on adaptive eigen subspace extraction method

Abstract

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.