Improved process monitoring based on global–local manifold analysis and statistical local approach for industrial process. (March 2019) - Zhan, Chengjun; Li, Shuanghong; Yang, Yupu
Highlights: A novel process monitoring method based on manifold analysis and statistical local approach is proposed. Two augmented vectors based on eigenvectors that represent the normal process behavior are derived. A visual fault diagnosis method based on the global–local manifold structure analysis is proposed. Fault detection and diagnosis by the proposed method are tested through a numerical example and the TE process. Abstract: A new kind process monitoring method which based on global–local manifold analysis is proposed in this article. Firstly, this method models the global–local manifold structure of the process in the off-line stage, and then detects the changes in the global–local manifold structure when new observation samples are used for on-line monitoring. In addition, conventional algorithms that based on global–local manifold structure may lose accuracy when monitor the process since the Gaussian assumption is not easily satisfied. In this new method, a novel score variable which approximately follows Gaussian distribution regardless of the original is derived. Besides, the incorporation of statistical local approach also reveals conspicuous detection sensitivity. We also propose a visual fault diagnosis method based on the global–local manifold structure analysis. The proposed process monitoring method is well examined through a numerical example and the TE process benchmark.