Three-layer deep learning network random trees for fault detection in chemical production process

TL;DR

This paper introduces TDLN-trees, a novel fault detection model combining deep learning and machine learning techniques to improve fault detection accuracy in complex chemical production processes.

Contribution

It proposes a three-layer deep learning network random trees model that integrates LSTM, fully connected neural networks, and extra trees for enhanced fault detection.

Findings

Outperforms existing fault detection methods in accuracy

Effectively extracts and classifies temporal features

Validated on Tennessee Eastman process data

Abstract

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.