Fault diagnosis for blast furnace ironmaking process based on randomized local fisher discriminant analysis

Fault diagnosis plays a vital role in ensuring the operation safety of blast furnaces and improving the quality of molten iron in the ironmaking and steelmaking industry. The blast furnace ironmaking process (BFIP) is intrinsically nonlinear. To address the nonlinearity issue of BFIP, a novel fault diagnosis approach that combines the randomized method, local structure information, and Fisher discriminant analysis is proposed in this paper. Using a randomized feature map, the process data is first mapped onto a randomized explicit low-dimensional feature space. Compared to kernel methods, explicit low-dimensional random Fourier features considerably reduce the computational cost, particularly for real-time fault diagnosis for a large training dataset or a large-scale process. Additionally, the local structure information contained in the randomized low-dimensional feature space is extracted. The fault diagnosis performance is improved through the exploration of the local structure of random Fourier features. Finally, the blast furnace iron-marking process state is determined using Bayesian inference. Case studies on a real-world BFIP are carried out to demonstrate the superior performance of the proposed method in comparison with other related methods.