Fault diagnosis hybrid system using a Luenberger-based detection filter and neural networks

The present paper proposes a new layout for failure detection and diagnosis in industrial dynamic systems in which, failure vector decoupling is not always possible, due the failure intrinsic propagation. In this case diagnosis can be determined due to the existing correlation between the failure vector and residual vector time patterns. The greatest benefit of this study is the failure detection method (Luenberger observer based detection filter) through vectorial residual generation combined with the pattern recognition technique based on neural networks theory. The synergy of both methods offer a wider application range to diagnosis problem solutions, in systems under the presence of non-decoupled failures.