Industrial process data are naturally complex time series with high nonlinearities and dynamics. To model nonlinear dynamic processes, a long short-term memory (LSTM) network is very suitable for soft sensor model development. However, the original LSTM does not consider variable and sample relevance for quality prediction. In order to overcome this problem, a spatiotemporal attention-based LSTM network is proposed for soft sensor modeling, which can, not only identify important input variables that are related to the quality variable at each time step, but also adaptively discover quality-related hidden states across all time steps. By taking the spatiotemporal quality-relevant interactions into consideration, the prediction performance can be improved for the soft sensor model. The effectiveness and flexibility of the proposed model is demonstrated on an industrial hydrocracking process to predict the initial boiling points of heavy naphtha and aviation kerosene.

中文翻译：

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基于时空注意力的 LSTM 深度学习用于工业软传感器模型开发

工业过程数据自然是具有高度非线性和动态性的复杂时间序列。为了模拟非线性动态过程，长短期记忆 (LSTM) 网络非常适合软传感器模型开发。但是，原始 LSTM 没有考虑变量和样本相关性进行质量预测。为了克服这个问题，提出了一种基于时空注意力的 LSTM 网络用于软传感器建模，它不仅可以在每个时间步识别与质量变量相关的重要输入变量，还可以自适应地发现与质量相关的隐藏所有时间步长的状态。通过考虑时空质量相关的交互，可以提高软传感器模型的预测性能。