%0 Journal Article %1 noauthororeditor %A Zhen Luo, Huajing Fang, Xiaoyong Liu %A Chen, Xuquan %D 2014 %J Circuits and Systems: An International Journal (CSIJ) %K Gustafson-Kessel Kalman Unscented algorithm, clustering data detection driven, fault filter, fuzzy modeling, %N 3 %P 1-12 %T Fault detection based on novel fuzzy modelling %U https://airccse.com/csij/papers/1314csij01.pdf %V 1 %X The Fault detection which is based on fuzzy modeling is investigated. Takagi-Sugeno (TS) fuzzy model can be derived by structure and parameter identification, where only the input-output data of the identified system are available. In the structure dentification step, Gustafson-Kessel clustering algorithm (GKCA) is used to detect clusters of different geometrical shapes in the data set and to obtain the point-wise membership function of the premise. In the parameter identification step, Unscented Kalman filter (UKF) is used to estimate the parameters of the premise’s membership function. In the consequence part, Kalman filter (KF) algorithm is applied as a linear regression to estimate parameters of the TS model using the input-output data set. Then, the obtained fuzzy model is used to detect the fault. Simulations are provided to demonstrate the effectiveness of the theoretical results.

@article{noauthororeditor, abstract = {The Fault detection which is based on fuzzy modeling is investigated. Takagi-Sugeno (TS) fuzzy model can be derived by structure and parameter identification, where only the input-output data of the identified system are available. In the structure dentification step, Gustafson-Kessel clustering algorithm (GKCA) is used to detect clusters of different geometrical shapes in the data set and to obtain the point-wise membership function of the premise. In the parameter identification step, Unscented Kalman filter (UKF) is used to estimate the parameters of the premise’s membership function. In the consequence part, Kalman filter (KF) algorithm is applied as a linear regression to estimate parameters of the TS model using the input-output data set. Then, the obtained fuzzy model is used to detect the fault. Simulations are provided to demonstrate the effectiveness of the theoretical results. }, added-at = {2019-06-28T07:18:08.000+0200}, author = {{Zhen Luo, Huajing Fang}, Xiaoyong Liu and Chen, Xuquan}, biburl = {https://www.bibsonomy.org/bibtex/2b94701f59f18b866e1710a91c54c25c3/csijjournal1821}, interhash = {fe392c1dc21cdafa51a7a149dd4e9c7c}, intrahash = {b94701f59f18b866e1710a91c54c25c3}, journal = {Circuits and Systems: An International Journal (CSIJ)}, keywords = {Gustafson-Kessel Kalman Unscented algorithm, clustering data detection driven, fault filter, fuzzy modeling,}, month = {July}, number = 3, pages = {1-12}, timestamp = {2019-06-28T07:18:08.000+0200}, title = {Fault detection based on novel fuzzy modelling }, url = {https://airccse.com/csij/papers/1314csij01.pdf}, volume = 1, year = 2014 }