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Enhanced Data-Driven Fault Diagnosis of Chemical Process via Information Fusion in Multiple Neural Networks and Andrews Plot

Lookup NU author(s): Dr Shengkai Wang, Dr Jie ZhangORCiD


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An enhanced data-driven fault diagnosis system combining multiple neural networks and using Andrews plot for information pre-processing is presented in this paper. The system first extract features from on-line measurements by Andrews plot approach and the normalized features are used as the neural network inputs. To overcome the non-robustness of a single neural network, multiple neural networks are combined using information fusion for enhanced fault diagnosis performance. The proposed method is applied to a simulated CSTR process and the results show that the proposed method gives more robust and faster diagnosis than the conventional neural network based fault diagnosis method.

Publication metadata

Author(s): Wang S, Zhang J

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 26th International Conference on Automation and Computing (ICAC’21)

Year of Conference: 2021

Print publication date: 02/09/2021

Online publication date: 15/11/2021

Acceptance date: 02/07/2021

ISSN: 9781665443524

Publisher: IEEE


DOI: 10.23919/ICAC50006.2021.9594242