  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.
