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Research Article Open access CC BY 4.0

Research Progress of Artificial Neural Network and Its Application in Fault Diagnosis of Chemical Industry

Zhihui Zhao, Jiying Li

Journal of Engineering Research and Reports · pp. 363–372 · Published 26 Dec 2022

10.9734/jerr/2022/v23i12791

Abstract

Many characteristics exhibited by artificial neural networks, such as nonlinearity, large scale, strong parallel processing ability, as well as robustness, fault tolerance, and strong self-learning ability, make it attractive for fault detection and diagnosis in complex systems. The relationship between the complex process, cumbersome process, and measurable process variable failure causes of chemical process is very complicated. Once a failure occurs, it will cause huge economic losses and casualties. The emergence of artificial neural network provides a new chemical fault diagnosis technology, which can carry out early and accurate fault detection and diagnosis for chemical process and equipment, so as to improve the efficiency and safety of production. This paper introduces the basic principle and development history of artificial neural network, as well as several typical artificial neural networks, such as back propagation algorithm (BP network), radial basis network (RBF network), and their application in chemical process fault diagnosis.

Artificial neural network fault diagnosis chemical process BP network RBF network

References (37)

  1. 1 A Fast Learning Algorithm for Deep Belief Nets [DOI]
  2. 2 The perceptron: A probabilistic model for information storage and organization in the brain. [DOI]
  3. 3 The Organization of Behavior [DOI]
  4. 4 Perceptrons: An Introduction to Computational Geometry
  5. 5 Artificial neural network models of knowledge representation in chemical engineering [DOI]
  6. 6 A neural network methodology for process fault diagnosis [DOI]
  7. 7 Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications [DOI]
  8. 8 FERNN: An Algorithm for Fast Extraction of Rules from Neural Networks [DOI]
  9. 9 Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee–Eastman process [DOI]
  10. 10 Nonparallel Emotional Speech Conversion [DOI]
  11. 11 Neural networks in process fault diagnosis [DOI]
  12. 12 Incipient fault diagnosis of chemical processes via artificial neural networks [DOI]
  13. 13 Computational power of neural networks: a characterization in terms of Kolmogorov complexity [DOI]
  14. 14 Some analytical solutions to the general approximation problem for feedforward neural networks [DOI]
  15. 15 An affine invariant approach for dense wide baseline image matching [DOI]
  16. 16 Process fault detection and diagnosis using neural networks—I. steady-state processes [DOI]
  17. 17 Fault diagnosis in complex chemical plants using artificial neural networks [DOI]
  18. 18 Adaptive networks for fault diagnosis and process control [DOI]
  19. 19 APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN PROCESS FAULT DIAGNOSIS [DOI]
  20. 20 Neural network based framework for fault diagnosis in batch chemical plants [DOI]
  21. 21 Fault diagnosis of chemical processes considering fault frequency via Bayesian network [DOI]
  22. 22 Artificial Neural Network and Its Application Research Progress in Chemical Process [DOI]
  23. 23 Application of Artificial Neural Networks in Chemical Process Control [DOI]
  24. 24 Application of Neural Network Algorithm in Propylene Distillation [DOI]
  25. 25 An approach to fault diagnosis of chemical processes via neural networks [DOI]
  26. 26 Embedding Color Watermark by Adjusting DCT using RGB Gray Scale Watermarking [DOI]
  27. 27 Abnormal Event Identification in Nuclear Power Plants Using a Neural Network and Knowledge Processing [DOI]
  28. 28 Dynamic fault detection and diagnosis using neural networks [DOI]
  29. 29 Application Progress of Artificial Neural Network in Chemical Industry [DOI]
  30. 30 Face Recognition Technology Based on Neural Network: A Review [DOI]
  31. 31 A STUDY OF THE CONVERGENCE OF THE CMAC LEARNING PROCESS
  32. 32 Fault Diagnosis of Chemical Process Based on ACO-BP Neural Network [DOI]
  33. 33 Process fault detection and diagnosis in CSTR system using on-line approximator [DOI]
  34. 34 IEEE Transactions on Information Theory [DOI]
  35. 35 Journal of Engineering Research and Reports [DOI]
  36. 36 Asian Journal of Research in Computer Science [DOI]
  37. 37 Application of Neural Network Algorithm in Propylene Distillation [DOI]

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