Open AccessOrthotopic‐filtering‐based hierarchical fault diagnosis algorithm for linear recursive models
Author(s) -
Wang Ziyun,
Xu Guixiang,
Wang Yan,
Park Ju H.,
Ji Zhicheng
Publication year - 2020
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2019.1229
Subject(s) - fault (geology) , cluster analysis , fault coverage , algorithm , computer science , fault model , linear discriminant analysis , stuck at fault , fault detection and isolation , set (abstract data type) , fault indicator , hierarchical clustering , pattern recognition (psychology) , data mining , artificial intelligence , engineering , electronic circuit , electrical engineering , seismology , actuator , programming language , geology
An orthotopic‐filtering‐based hierarchical fault diagnosis algorithm is proposed for complex systems with multiple fault types. The given algorithm uses an orthotopic method to describe a feasible parameter set and detects whether a fault occurs by determining whether the feasible parameter set is empty. Then the hierarchical clustering method is applied to analyse the fault library. The clustering result is used as the prior knowledge for fault diagnosis analysis, and a discriminant analysis is conducted layer by layer. Finally, a model‐matching method is applied to realise the fault identification. However, if the fault type is not included in the fault library, the fault type is then added to the fault library. Therefore, when the fault diagnosis is performed again, the fault library is re‐hierarchically clustered. The analysis on the false alarm rate and the missing detection rate of the fault diagnosis algorithm are also studied. Finally, the fault diagnosis of a buck circuit is taken as an example to demonstrate the effectiveness and feasibility of the proposed method by analysing the fault diagnosis results.