Improvement of fault diagnosis efficiency in nuclear power plants using hybrid intelligence approach

• Published in: Progress in nuclear energy (New series) 2014-09, Vol.76, p.122-136
• Main Authors: Liu, Yong-kuo, Xie, Chun-li, Peng, Min-jun, Ling, Shuang-han
• Format: Article
• Language: English
• Subjects: Artificial neural network; Data fusion; Fault diagnosis; Faults; Graph theory; Hybrid systems; Intelligence; Nuclear engineering; Nuclear power generation; Nuclear power plant; Nuclear power plants; Signed directed graph (SDG)
• ISSN: 0149-1970
• DOI: 10.1016/j.pnucene.2014.05.001

Different types of faults could occur in a nuclear power plant, and there was no direct correspondence between a specific fault and its symptoms. So, a hybrid intelligence approach is proposed for the fault diagnosis at a nuclear power plant. Depending up the symptoms observed and the progress of fault diagnosis process, different fault diagnosis technologies, such as artificial neural network, data fusion and signed directed graph, could be combined as appropriate to detect and identify different faults at local or global level in nuclear power plants. The effectiveness of hybrid intelligence approach in improving the fault diagnosis efficiency in nuclear power plants was verified through simulation experiments. •To the different diagnostic object, we adopted the different diagnostic methods.•We designed the structure of distributed diagnosis system.•The system can achieve local diagnosis, global diagnosis and the fault path verification.•A single fault, double faults and some multiple faults diagnosis can be achieved in system.