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

arXiv:2405.00311v1 Announce Type: new
Abstract: 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 …

abstract arxiv complexities cs.lg current deep learning development diagnosis diagnostic however layer making network paper process processes production production processes random scale struggle technology trees type