Faults occurring in the production line can cause many losses. Predicting the fault events before they occur or identifying the causes can effectively reduce such losses. A modern production line can provide enough data to solve the problem. However, in the face of complex industrial processes, this problem will become very difficult depending on traditional methods. In this paper, we propose a new approach based on a deep learning (DL) algorithm to solve the problem. First, we regard these process data as a spatial sequence according to the production process, which is different from traditional time series data. Second, we improve the long short-term memory (LSTM) neural network in an encoder-decoder model to adapt to the branch structure, corresponding to the spatial sequence. Meanwhile, an attention mechanism (AM) algorithm is used in fault detection and cause identification. Third, instead of traditional biclassification, the output is defined as a sequence of fault types. The approach proposed in this article has two advantages. On the one hand, treating data as a spatial sequence rather than a time sequence can overcome multidimensional problems and improve prediction accuracy. On the other hand, in the trained neural network, the weight vectors generated by the AM algorithm can represent the correlation between faults and the input data. This correlation can help engineers identify the cause of faults. The proposed approach is compared with some well-developed fault diagnosing methods in the Tennessee Eastman process. Experimental results show that the approach has higher prediction accuracy, and the weight vector can accurately label the factors that cause faults.

中文翻译：

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基于深度学习的复杂工业过程故障预测与原因识别方法

生产线中发生的故障可能会造成很多损失。在故障事件发生之前对其进行预测或找出原因可以有效地减少此类损失。现代化的生产线可以提供足够的数据来解决问题。然而，面对复杂的工业过程，取决于传统方法，该问题将变得非常困难。在本文中，我们提出了一种基于深度学习（DL）算法的新方法来解决该问题。首先，根据生产过程，我们将这些过程数据视为空间序列，这与传统的时间序列数据不同。其次，我们在编解码器模型中改进了长短期记忆（LSTM）神经网络，以适应与空间序列相对应的分支结构。同时，注意机制（AM）算法用于故障检测和原因识别。第三，代替传统的双分类，将输出定义为一系列故障类型。本文提出的方法有两个优点。一方面，将数据视为空间序列而不是时间序列可以克服多维问题并提高预测精度。另一方面，在训练后的神经网络中，由AM算法生成的权重向量可以表示故障与输入数据之间的相关性。这种相关性可以帮助工程师确定故障原因。将该方法与田纳西伊士曼过程中一些完善的故障诊断方法进行了比较。实验结果表明，该方法具有较高的预测精度，