Semantic Scholar Open Access 2018 455 sitasi

Deep convolutional neural network model based chemical process fault diagnosis

Hao Wu Jinsong Zhao

Abstrak

Abstract Numerous accidents in chemical processes have caused emergency shutdowns, property losses, casualties and/or environmental disruptions in the chemical process industry. Fault detection and diagnosis (FDD) can help operators timely detect and diagnose abnormal situations, and take right actions to avoid adverse consequences. However, FDD is still far from widely practical applications. Over the past few years, deep convolutional neural network (DCNN) has shown excellent performance on machine-learning tasks. In this paper, a fault diagnosis method based on a DCNN model consisting of convolutional layers, pooling layers, dropout, fully connected layers is proposed for chemical process fault diagnosis. The benchmark Tennessee Eastman (TE) process is utilized to verify the outstanding performance of the fault diagnosis method.

Topik & Kata Kunci

Computer Science

Penulis (2)

Hao Wu

Jinsong Zhao

Format Sitasi

APA MLA BibTeX

Wu, H., Zhao, J. (2018). Deep convolutional neural network model based chemical process fault diagnosis. https://doi.org/10.1016/j.compchemeng.2018.04.009

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Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 455×
Sumber Database: Semantic Scholar
DOI: 10.1016/j.compchemeng.2018.04.009
Akses: Open Access ✓