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Distributed output‐feedback fault detection and isolation of cascade process networks
Author(s) -
Yin Xunyuan,
Liu Jinfeng
Publication year - 2017
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.15791
Subject(s) - residual , control theory (sociology) , cascade , fault detection and isolation , estimator , fault (geology) , process (computing) , state (computer science) , generator (circuit theory) , control engineering , actuator , engineering , noise (video) , computer science , control (management) , power (physics) , mathematics , algorithm , artificial intelligence , statistics , physics , quantum mechanics , chemical engineering , seismology , image (mathematics) , geology , operating system
Distributed output‐feedback fault detection and isolation (FDI) of nonlinear cascade process networks that can be divided into subsystems is considered. Based on the assumption that an exponentially convergent estimator exists for each subsystem, a distributed state estimation system is developed. In the distributed state estimation system, a compensator is designed for each subsystem to compensate for subsystem interaction and the estimators for subsystems communicate to exchange information. It is shown that when there is no fault, the estimation error of the distributed estimation system converges to zero in the absence of system disturbances and measurement noise. For each subsystem, a state predictor is also designed to provide subsystem state predictions. A residual generator is designed for each subsystem based on subsystem state estimates given by the distributed state estimation system and subsystem state predictions given by the predictor. A subsystem residual generator generates two residual sequences, which act as references for FDI. A distributed FDI mechanism is proposed based on residuals. The proposed approach is able to handle both actuator faults and sensor faults by evaluating the residual signals. A chemical process example is introduced to demonstrate the effectiveness of the distributed FDI mechanism. © 2017 American Institute of Chemical Engineers AIChE J , 63: 4329–4342, 2017