Minjia Krueger

A Canonical Variate Analysis Based Process Monitoring Scheme and Benchmark Study

Abstract Principal component analysis (PCA) and Partial least square (PLS) are powerful multivariate statistical tools that have been successfully applied for process monitoring. They are efficient in dimension reduction and are suitable for processing large amount of data. Nevertheless, their application scope is restricted to static processes where the dynamics are ignored. In order to achieve improved monitoring performance for dynamic processes, in this paper, we propose an effective dynamic monitoring scheme based on the canonical variate analysis (CVA) technique. Different from the standard PCA- and PLS-based techniques which rely on mean-extraction for residual generation, the proposed CVA-based scheme takes process dynamics into account as well. The properties of all three methods are then compared in detail and finally, the improvements of the proposed method are demonstrated on the well-accepted Tennessee Eastman benchmark process.

Online Monitoring System Design for Roll Eccentricity in Rolling Mills

This paper deals with the parameter estimation and online monitoring of roll eccentricity in rolling mills. Roll-eccentricity-induced disturbances can result in strip thickness deviation and product quality degradation, and a conventional control strategy cannot regulate this kind of periodic disturbances due to its complex characteristics. For the purpose of product quality assessment and monitoring, a performance indicator based on product expectations or the empirical value of quality-related process variables, which can be quantitatively visualized with four zones, is proposed in the high level. In the low level, the key parameters of roll eccentricity are estimated in real time using an adaptive observer for frequency estimation and an adaptive algorithm for amplitude and phase estimation. The performance and effectiveness of the proposed eccentricity monitoring system are demonstrated through a case study of a cold rolling mill from industrial fields.

A Data-Driven Computation Method for the Gap Metric and the Optimal Stability Margin

Gap metric and stability margin have been proven as important model-based analysis tools for linear time-invariant (LTI) systems. Due to the need for an accurate model, both the gap metric and the optimal stability margin are mostly used for offline analysis. In online analysis, accurate models are rarely available, whereas measurement data could be easily obtained from the systems under consideration. Therefore, in this paper, an approach toward the calculation of the gap metric and the optimal stability margin based on the available measurement data of LTI systems is proposed. A data-driven realization of the so-called stable kernel and stable image representation serves as the foundation of this framework.

A Data-Driven Maintenance Support System for Wind Energy Conversion Systems

Abstract With the rapid growth of wind energy installed capacity, optimized maintenance has gained increasingly attentions from both researchers and wind farm owners. Condition-based maintenance (CBM) has been introduced to the wind energy industry in order to ensure the availability and safety of the wind energy conversion (WEC) system, while minimize the operating and maintenance (O&M) costs. In this paper a maintenance decision support system is introduced. By combining the information delivered by the data-driven WEC condition monitoring system and the economical benefits of each possible corrective maintenance action, the decision support system provides the operators with a choice of the most proper maintenance action for the current situation. The performance of the decision support system is tested with data collected from different WECs in a wind farm.

Real-Time Optimization of Automatic Control Systems With Application to BLDC Motor Test Rig

Driven by the increasing demands on production quality, system performance, and the reliability and safety issues of process industry, this paper proposes an integrated process monitoring and control design technique for industrial control systems. The proposed approach is an alternative realization of Youla parameterization which allows the performance of the controlled systems to be improved without modifying or replacing the predesigned control systems, while the closed-loop stability is guaranteed. In addition, a residual signal is available for the fault detection and isolation purpose. The effectiveness and performance of the proposed approach are demonstrated on a brushless direct current motor test rig.

A finite-time-convergent fault-tolerant control and its experimental verification for DTS200 three-tank system

This paper presents a fault-tolerant continuous super-twisting control algorithm for systems of dimension more than one, subject to Lipshitzian and non-Lipshitzian bounded disturbances. The conditions of finite-time convergence of the entire system state to the origin are obtained. An experimental verification of the designed fault-tolerant algorithm is conducted for a DTS200 three-tank system through varying fault sources, disturbances, input conditions, and inter-tank connections.