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The blast furnace reactor has a large volume, and smelting occurs at high temperatures and high-pressure environments. The blast furnace reactor has a large volume, and smelting occurs at high temperatures and high-pressure environments. Considering the small samples of fault data and non-stationarity of the production process with a considerable time lag, it is difficult to diagnose and eliminate the faults timely. This paper proposes for the first time a data and knowledge collaborative-driven framework to identify fault and perform self-healing control. Following the adaptive two-scale sequence division method, the prior knowledge matrix refined K2 algorithm is used to construct Bayesian networks to model the control process. When a new abnormal condition arises, the most similar historical sequence will be found, and inference on the corresponding Bayesian network can lead to fault identification results and self-healing control action. The proposed framework has achieved satisfactory results in simulation experiments and real data experiments. In the simulation data, the Bayesian network (BN) modeling achieved an accuracy of 98.75%, and the time required for structure learning of the BN was reduced by over 75.69%. In real data, the accuracy of identifying blast furnace faults reached 97.1%, and the results of self-healing control reasoning surpassed those of traditional BN reasoning methods in terms of correctness, order, completeness, and necessity. • A self-healing control framework for blast furnace is proposed. • Introducing prior knowledge into industry can efficiently benefit process modeling. • Put forward an adaptive sequence division method for non-stationary data. • The method is suitable for non-stationary and high safety requirements situations. [ABSTRACT FROM AUTHOR]

Detecting early abnormalities in blast furnaces is important for the smooth operation of the iron-making process. In this paper, recursive transformed component statistical analysis (RTCSA)-based algorithms are developed to monitor the iron-making process with the task of early abnormality detection. The increments of variables instead of the absolute measurements are used for RTCSA, in order to decrease the effect of the time-varying nature of the process. Owing to the peak-like disturbances caused by the switching of hot blast stoves, an online identification algorithm is designed to locate the disturbance intervals. Then an index-switching scheme is used for monitoring the process. The effectiveness of the proposed method is verified using the real data of two blast furnaces. Compared with the conventional methods such as the two-stage principal component analysis, the increment-based RTCSA can effectively detect early abnormalities in the iron-making process. [ABSTRACT FROM AUTHOR]

This paper presents a fault detection method based on augmented kernel Mahalanobis distance (AKMD) for monitoring nonlinear dynamic processes. In order to reflect the information of dynamic correlations, the measurements are stacked into augmented vectors at adjacent sampling instants. The augmented kernel Mahalanobis distance serves as the detection index, and its control limit is determined by the empirical method with assigning a significance level. Contrary to the mainstream of process monitoring methods based on principal component analysis (PCA), dimensionality reduction is not used here. The disadvantage of dimensionality reduction and space partition is discussed, and the improvement of fault detectability via data augmentation is analyzed. In addition, the computational complexity of the proposed method is acceptable. For training dataset containing m variables and n samples, if n ≫ m , the online computational burden of the proposed method is about O ( n 2 ) . Simulations about a nonlinear dynamic process and the benchmark Tennessee Eastman process (TEP) both illustrate higher detection rates of the proposed method, compared with conventional multivariate statistical process monitoring (MSPM) methods such as PCA and its variants. [ABSTRACT FROM AUTHOR]