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Detector design for active fault diagnosis in closed‐loop systems
Author(s) -
Sekunda A. K.,
Niemann H. H.,
Poulsen N. K.
Publication year - 2018
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2858
Subject(s) - residual , parametric statistics , fault detection and isolation , control theory (sociology) , fault (geology) , detector , signal (programming language) , engineering , computer science , white noise , control engineering , reliability engineering , algorithm , artificial intelligence , mathematics , control (management) , statistics , telecommunications , seismology , actuator , programming language , geology
Summary Fault diagnosis of closed‐loop systems is extremely relevant for high‐precision equipment and safety critical systems. Fault diagnosis is usually divided into 2 schemes: active and passive fault diagnosis. Recent studies have highlighted some advantages of active fault diagnosis based on dual Youla‐Jabr‐Bongiorno‐Kucera parameters. In this paper, a method for closed‐loop active fault diagnosis based on statistical detectors is given using dual Youla‐Jabr‐Bongiorno‐Kucera parameters. The goal of this paper is 2‐fold. First, the authors introduce a method for measuring a residual signal subject to white noise. Second, an optimal detector design is presented for single and multiple faults using the amplitude and phase shift of the residual signal to conduct diagnosis. Here, both the optimal case of a perfect model and the suboptimal case of a model with uncertainties are discussed. The method is successfully tested on a simulated system with parametric faults.