Hot-rolled strip products have diverse applications, and enhancing the detection, diagnostics, and prognostics of product quality during hot rolling is essential. Nevertheless, the multivariable, strong coupling, nonlinear, and time-varying nature of the production process poses a rigorous challenge for accurate hot-rolled prognostics. This paper implements a progressive layered extraction (PLE) multi-task learning (MTL) framework to simultaneously estimate multiple quality indicators, such as strip crown, center line deviation, exit temperature, wedge, width, and symmetry flatness. Additionally, the paper proposes the implements of Hybrid Bayesian Neural Network (HBNN) experts and a gating network with attention mechanism to integrate private and shared task features. It also puts forth an auxiliary task involving a Variational Autoencoder with Generative Adversarial Networks (VAE-GAN) to extract latent states from the original sequence. Moreover, an adaptive joint loss optimization is employed to update the weight of individual task losses for MTL training problems, and three sets of field hot-rolled datasets are used for model evaluation. In the experimental validation, considering the noisy field data and limited conditions in the real hot rolled production, comparative experiments are conducted to demonstrate the improved generalization and robustness of the proposed MTL approach. These experiments involve different percentages of the total data, ranging from 5% to 20%, and various prediction horizons ranging from 1 to 50 steps for model establishment. In addition, the paper discusses the interpretation of the model and strategies for further enhancing model performance.

中文翻译：

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基于混合贝叶斯渐进分层提取多任务学习的热轧预测方法

热轧带钢产品具有多种应用，加强热轧过程中产品质量的检测、诊断和预测至关重要。然而，生产过程的多变量、强耦合、非线性和时变特性对准确的热轧预测提出了严峻的挑战。本文实现了渐进分层提取（PLE）多任务学习（MTL）框架来同时估计多个质量指标，例如带材凸度、中心线偏差、出口温度、楔形、宽度和对称平坦度。此外，本文还提出了混合贝叶斯神经网络（HBNN）专家和具有注意机制的门控网络的实现，以集成私有和共享任务特征。它还提出了一项辅助任务，涉及带有生成对抗网络的变分自动编码器（VAE-GAN），以从原始序列中提取潜在状态。此外，采用自适应联合损失优化来更新 MTL 训练问题的各个任务损失的权重，并使用三组现场热轧数据集进行模型评估。在实验验证中，考虑到实际热轧生产中的噪声现场数据和有限条件，进行了对比实验，以证明所提出的 MTL 方法具有改进的泛化性和鲁棒性。这些实验涉及总数据的不同百分比（从 5% 到 20%），以及用于模型建立的各种预测范围（从 1 到 50 个步骤）。此外，论文还讨论了模型的解释以及进一步增强模型性能的策略。