With the development of technology, the chemical production process is becoming increasingly complex and large-scale, making fault detection particularly important. However, current detective 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 detection 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大型化，故障检测显得尤为重要。然而，当前的检测方法难以应对大型生产过程的复杂性。本文综合深度学习与机器学习技术的优势，结合双向长短期记忆神经网络、全连接神经网络以及极端随机树算法的优点，提出了一种名为三层深度学习网络随机树的新型故障检测模型。首先，深度学习组件从工业数据中提取时序特征，并将其组合转化为更高层次的数据表示。其次，机器学习组件对第一步提取的特征进行处理与分类。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。