Abstract With the development of the chemical industry, fault diagnosis of chemical processes has become a challenging problem because of the high-dimensional data and complex time correlation caused by the more complex chemical processes and increasing number of equipment. However, the ordinary feedforward neural network cannot solve these problems very well. Therefore, this paper proposes a fault diagnosis model based on the optimized long short-term memory (LSTM) network. Since the number of hidden layer nodes in the LSTM network has a great influence on the diagnosis result, the link of determining the optimal number of hidden layer nodes by the iterative method based on the LSTM network is added. Then the LSTM is optimized to get higher chemical process fault diagnosis accuracy. Finally, through the simulation experiment of the Tennessee Eastman (TE) chemical process, the results verify that the optimized LSTM network has better performance in chemical process fault diagnosis than the BP neural network, the multi-layer perceptron method and the original LSTM network.

中文翻译：

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基于优化长短期记忆网络的化工过程故障诊断模型

摘要 随着化学工业的发展，化学过程的复杂化和设备数量的增加导致数据的高维和复杂的时间关联性，使得化学过程的故障诊断成为一个具有挑战性的问题。然而，普通的前馈神经网络并不能很好地解决这些问题。因此，本文提出了一种基于优化的长短期记忆（LSTM）网络的故障诊断模型。由于LSTM网络中隐藏层节点数对诊断结果影响较大，因此增加了基于LSTM网络通过迭代法确定最佳隐藏层节点数的环节。然后对 LSTM 进行优化，以获得更高的化学过程故障诊断精度。最后，