Executing various production tasks is critical to the safe operation and efficient production of industrial processes. As one of them, the detection task of key quality variables directly affects the operation optimization and decision-making of industrial processes, but it is severely limited by the harsh environment and detection instruments. Therefore, the real-time prediction task of key quality variables becomes the basis for optimal control of industrial processes. To address this issue, this paper proposes a task-oriented deep learning framework based on a target-related transformer (TR-Former) network for industrial quality prediction tasks. Specifically, a new target-related self-attention (TR-SA) mechanism is developed to guide feature learning by adding attention scores between task-related target variables and other variables. As a result, the learned features in this instance will be guaranteed to be relevant to the target variable and useful for the quality prediction task. Moreover, the long-range dynamics of industrial process data can also be captured, which can further improve the prediction performance of the model. Finally, extensive experiments were conducted on two industrial processes to validate the superiority of the proposed method in terms of quality prediction tasks. The experimental results demonstrate that the proposed TR-Former method exhibits an improvement ranging from 3% to 13% in the mean absolute error indicator compared to the traditional transformer and other state-of-the-art methods.

中文翻译：

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基于目标相关变压器网络的面向任务的深度学习框架，用于工业质量预测应用

执行各种生产任务对于工业过程的安全运行和高效生产至关重要。作为其中之一，关键质量变量的检测任务直接影响工业过程的运行优化和决策，但受到恶劣环境和检测仪器的严重限制。因此，关键质量变量的实时预测任务成为工业过程优化控制的基础。为了解决这个问题，本文提出了一种基于目标相关变压器（TR-Former）网络的面向任务的深度学习框架，用于工业质量预测任务。具体来说，开发了一种新的目标相关自注意力（TR-SA）机制，通过在任务相关目标变量和其他变量之间添加注意力分数来指导特征学习。因此，在这种情况下学习到的特征将被保证与目标变量相关并且对于质量预测任务有用。而且，还可以捕获工业过程数据的远程动态，这可以进一步提高模型的预测性能。最后，对两个工业过程进行了广泛的实验，以验证所提出的方法在质量预测任务方面的优越性。实验结果表明，与传统 Transformer 和其他最先进的方法相比，所提出的 TR-Former 方法的平均绝对误差指标提高了 3% 至 13%。