In this study, parameter faults in a class of non-identifiable closed-loop multiple-input multiple-output systems are considered. A new parameter estimation-based fault diagnosis method is proposed. It is known that in open-loop systems, the system parameters can be identified directly and the on-line identification results can be used for fault detection and isolation. However, as the closed-loop system is non-identifiable because of the correlation introduced by the controller, unique optimal parameter estimation solution cannot be obtained. To address such an issue, a new method to detect and isolate parameter faults of closed-loop systems without persistent excitation condition is proposed. A reduced-order model is firstly constructed, which is the projection of the original model onto the orthogonal direction of the controller. By doing this, the aforementioned correlation can be successfully removed. The parameters of the newly constructed model, called as feature parameters, are then identified. The physical faults are finally detected and isolated based on the on-line identification results of the feature parameters, the projection direction and the known influence matrix. Simulation results are given to show the effectiveness of the proposed method.

Monitoring an ironmaking process is a very challenging task as it often fluctuates frequently and lacks of direct measurements. Principal component analysis (PCA) technique has been widely used in various industrial fields, mainly due to its advantage of not requiring the information about the principle knowledge of the process and faults. However, the PCA based application results in ironmaking process are still limited. In this paper, based on the dataset collected from a real blast furnace with a volume of 2 000 m 3 , a fault diagnosis method by incorporating the PCA technique in two stages will be presented. To overcome the adverse effects of the peak-like disturbances caused by switching between two distinct hotblast stoves, they are identified and removed from the dataset through the first-stage PCA. Experimental results show that our method outperforms the existing algorithm and the operators' monitoring in detecting the getting cold accident of the blast furnace.

Qualitative trend analysis (QTA) is an effective tool for process data analysis, the applications of which can be found in a variety of fields, such as process monitoring, fault diagnosis, and data mining. Reliable and accurate trend extraction of sensor data is the first and indispensable step in QTA. In this article, a new trend extraction algorithm is developed that is based on global optimization of the polynomial fit of the process data. Different from most existing works, this newly proposed algorithm solves the trend extraction task by simultaneously and globally estimating the episode number, the boundary time points of the episode, and the fitted polynomial coefficients, which shows improved performance over other nonglobally optimal trend extraction algorithms and requires less a priori knowledge than the existing globally optimal trend algorithms. The effectiveness of the algorithm is illustrated by testing on a variety of simulation and real blast furnace data. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3374–3383, 2017

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