The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Deep convolutional neural network model based chemical process fault diagnosis
Abstract
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.
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Cited by
Recent advances in sensor fault diagnosis: a review
Li, D., Wang, Y., Wang, J., Wang, C., & Duan, Y. (2020) · Sensors and Actuators A Physical · doi:10.1016/j.sna.2020.111990 · Cited by 323
Metadata
- Authors
- Wu, H., & Zhao, J.
- Venue
- Computers & Chemical Engineering
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 115
- Pages
- 185-197
- ISSN
- 0098-1354 (ISSN-L)
- OpenAlex
- W2796942168
- Cited by
- 517
- References
- 47
- FWCI
- 29.17
- Type
- journal article
- Language
- en
- Open access
- closed
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