With the development of technology, the chemical production process is becoming increasingly complex and large-scale, making fault diagnosis particularly important. However, current diagnostic methods struggle to address the complexities of large-scale production processes. In this paper, we integrate the strengths of deep learning and machine learning technologies, combining the advantages of bidirectional long and short-term memory neural networks, fully connected neural networks, and the extra trees algorithm to propose a novel fault diagnostic model named three-layer deep learning network random trees (TDLN-trees). First, the deep learning component extracts temporal features from industrial data, combining and transforming them into a higher-level data representation. Second, the machine learning component processes and classifies the features extracted in the first step. An experimental analysis based on the Tennessee Eastman process verifies the superiority of the proposed method.

翻译：随着技术的发展，化工生产过程日益复杂化和规模化，使得故障诊断变得尤为重要。然而，当前的诊断方法难以应对大规模生产过程的复杂性。本文融合深度学习与机器学习技术的优势，结合双向长短时记忆神经网络、全连接神经网络以及额外树算法的特点，提出一种新型故障诊断模型，名为三层深度学习网络随机树（TDLN-trees）。首先，深度学习组件从工业数据中提取时间特征，并将其组合转换为更高级的数据表示。其次，机器学习组件对第一步提取的特征进行处理和分类。基于田纳西-伊斯曼过程的实验分析验证了所提方法的优越性。