Xiaochen Xie

TL;DR: A basic data-driven design framework with necessary modifications under various industrial operating conditions is sketched, aiming to offer a reference for industrial process monitoring on large-scale industrial processes.

TL;DR: A novel robust data-driven approach, named advanced partial least squares (APLS), is presented to handle process outliers under an improved framework of PLS and can remove the impact of outliers on process measurements and establish a more accurate model than PLS for fault diagnosis in the monitoring scheme.

TL;DR: The improved incremental learning approach based on available process measurements takes advantage of the algorithm overlapping of locally weighted projection regression (LWPR) and partial least squares (PLS) to cope with the KPI prognosis issue under nonlinear conditions.

TL;DR: The effectiveness of the proposed locally weighted total PLS monitoring approach is finally demonstrated by the comparisons with other relevant methods via simulations based on the wastewater treatment process benchmark under different abnormal conditions.

TL;DR: In this article, the authors focus on the asymptotic stability and stabilisation of uncertain linear systems with time-varying delays via delta operator approach and employ a new model formulation to transform the time-delayed delta operator system into an interconnected system for which the uncertainties can become easy to deal with.