An imbalanced number of faulty and normal samples causes serious damage to the performance of the conventional diagnosis methods. To settle the data-imbalance fault diagnosis problem, this article presents a novel general imbalanced sample selection strategy (DiagSelect) based on deep reinforcement learning. In DiagSelect, the problem of imbalanced sample selection from the training set is formulated as a multiarmed bandit problem of deep reinforcement learning. The nondifferentiable optimization problem of imbalanced sample selection can be solved by the Markov decision process. The parameters of DiagSelect can be optimized by REINFORCE with the feedback of the validation set. DiagSelect performs intelligent imbalanced sample selection to obtain better diagnosis performance autonomously. As a data-level technique, DiagSelect can be easily used in conjunction with the conventional diagnosis models. DiagSelect is validated in a synthetic dataset and an industrial process dataset. The results have shown the effectiveness, stability, and transferability of DiagSelect.

Sinter ore is the main raw material of the blast furnace, and burn-through point (BTP) has a direct influence on the yield, quality, and energy consumption of the ironmaking process. Since iron ore sintering is a very complex industrial process with strong nonlinearity, multivariable coupling, random noises, and time variation, traditional soft-sensor models are hard to learn the knowledge of the sintering process. In this article, a multistep prediction model, called denoising spatial–temporal encoder–decoder, is developed to predict BTP in advance. First, the mechanism analysis is carried out to determine the relevant-BTP variables, and the BTP prediction is defined as a sequence-to-sequence modeling problem. Second, motivated by the random noises of industrial data, a denoising gated recurrent unit (DGRU) is designed to alleviate the impact of noise by adding a denoising gate into the GRU. In this case, the encoder with DGRU can better extract the latent variables of original sequence data. Then, spatial–temporal attention is embedded into the decoder to simultaneously capture the time-wise and variable-wise correlations between the latent variables and the target variable BTP. Finally, the experimental results on the real-world dataset of a sintering process demonstrated that the integrated multistep prediction model is effective and feasible.

The blast furnace is a highly energy-intensive, highly polluting, and extremely complex reactor in the ironmaking process. Soft sensors are a key technolog

Unsupervised domain adaptation (UDA) has recently gained attention in fault diagnosis due to its ability to address domain shift problems arising from changes in working conditions. However, when faced with the continual domain shift problem inherent in real-world industries with dynamic working conditions, UDA often suffers from catastrophic forgetting. To address this challenge, we propose a novel replay-free continual UDA framework, CoUDA, for fault diagnosis under dynamic working conditions. In CoUDA, prototype contrastive learning is employed in source domain pre-training in order to improve the model generalization ability in preparation for the adaptation to the subsequent target domains. Then, source discriminator constraint is employed to ensure that the acquired source domain knowledge serves as an anchor, and source feature knowledge distillation is applied to prevent catastrophic forgetting without replay in sequential target domain adaptation. In addition, for better domain adaptation, local domain alignment and information entropy minimization are utilized to achieve fine-grained domain alignment. Experimental results demonstrate the superiority of the proposed CoUDA in achieving robust fault diagnosis under dynamic working conditions.

As a key thermal-state indicator of the iron ore sintering process, the content of ferrous oxide (FeO) in the finished sinter is directly related to product quality. Based on the massive data of sintering process, the data-driven soft sensor model provides a good choice for real-time FeO content detection. However, the complex characteristics of the data, including dynamics, nonlinearity, and multisource heterogeneity, are still the main obstacles to improving the modeling accuracy. To solve this problem, in this article, a multisource information fusion autoformer (MIF-Autoformer) model is introduced. First, feature-level information fusion and data-level information fusion are implemented based on the MIF strategy. Then, the comprehensive information of the sintering process is fed to the downstream autoformer model in a serial manner, which not only improves the information capacity but also provides additional prior information about the FeO content grade. This is helpful for autoformer to capture the complex temporal distributions in the sintering process. Finally, the proposed model is applied to a real sintering plant. Experimental results show that the hybrid image features provided by the MIF strategy have a general optimization effect on different competitive models, and MIF-Autoformer exhibits the lowest prediction error on the test set.