Open AccessSENSOR FUSION BASED UPON SELF ORGANIZING MAPS AND LOCAL KALMAN FILTERS FOR LOCAL FAULT DIAGNOSIS
Author(s) -
H. BENÍTEZ-PÉREZ,
M. VÁZQUEZ-HERNÁNDEZ,
A. DURÁN-CHAVESTI
Publication year - 2015
Publication title -
latin american applied research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.123
H-Index - 23
eISSN - 1851-8796
pISSN - 0327-0793
DOI - 10.52292/j.laar.2015.407
Subject(s) - kalman filter , state vector , fault (geology) , artificial intelligence , computer science , sensor fusion , state (computer science) , feature (linguistics) , fault detection and isolation , filter (signal processing) , pattern recognition (psychology) , nonlinear system , noise (video) , extended kalman filter , fusion , data mining , point (geometry) , computer vision , algorithm , mathematics , image (mathematics) , linguistics , philosophy , physics , geometry , classical mechanics , quantum mechanics , seismology , actuator , geology
A challenging strategy for fault diagnosis is related to data fusion and feature extraction such as having local models of the plant integrated through patterns. To pursuit this goal a group of Kalman Filters is proposed, one per sensor in order to estimate a state of the plant per sensor. Thereafter, a self-organized map processes the information in order to determine the response areas within the map. Moreover, Kalman filter auto tuning procedure, becomes an interesting issue due to correlated noise amongst them as well as local state dependency at this point this may call a pre-processing stage. Furthermore, once global state vector is produced, the classification stage takes place as long as this vector exists as post processing stage. The resulting maps determine areas of fault free scenarios or nonlinear and no healthy responses classified as faults. This last point is obtained through a high accuracy of the system.