Process fault is one of the main reasons that a system may appear unreliable, and it affects the safety of a system. The existence of different degrees of noise in the industry also makes it difficult to extract the effective features of the data for the fault diagnosis method based on deep learning. In order to solve the above problems, this paper improves the deep belief network (DBN) and iterates the optimal penalty term by introducing a penalty factor, avoiding the local optimal situation of a DBN and improving the accuracy of fault diagnosis in order to minimize the impact of noise while improving fault diagnosis and process safety. Using the adaptive noise reduction capability of an adaptive lifting wavelet (ALW), a practical chemical process fault diagnosis model (ALW-DBN) is finally proposed. Then, according to the Tennessee–Eastman (TE) benchmark test process, the ALW-DBN model is compared with other methods, showing that the fault diagnosis performance of the enhanced DBN combined with adaptive wavelet denoising has been significantly improved. In addition, the ALW-DBN shows better performance under the influence of different noise levels in the acid gas absorption process, which proves its high adaptability to different noise levels.

中文翻译：

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基于惩罚迭代优化的化工过程混合智能故障诊断策略

过程故障是系统出现不可靠的主要原因之一，它影响系统的安全。行业中不同程度噪声的存在也使得基于深度学习的故障诊断方法难以提取数据的有效特征。为了解决上述问题，本文改进了深度置信网络（DBN），通过引入惩罚因子迭代最优惩罚项，避免了DBN的局部最优情况，提高了故障诊断的准确性，以最小化噪声的影响，同时提高故障诊断和过程安全性。利用自适应提升小波（ALW）的自适应降噪能力，最终提出了实用的化工过程故障诊断模型（ALW-DBN）。然后，根据Tennessee-Eastman（TE）基准测试过程，将ALW-DBN模型与其他方法进行对比，表明增强型DBN结合自适应小波去噪的故障诊断性能得到了显着提升。此外，ALW-DBN在酸性气体吸收过程中在不同噪声级的影响下表现出更好的性能，证明了其对不同噪声级的高度适应性。