Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Robust multivariable feedback control
Robust multivariable feedback control
Readings in model-based diagnosis
Readings in model-based diagnosis
Qualitative modelling of linear dynamical systems with quantized state measurements
Automatica (Journal of IFAC)
Industrial Applications of Knowledge-Based Diagnosis
Industrial Applications of Knowledge-Based Diagnosis
Dependability: Basic Concepts and Terminology
Dependability: Basic Concepts and Terminology
The Adaptable Distributed Recovery Block Scheme And A Modular Implementation Model
PRFTS '97 Proceedings of the 1997 Pacific Rim International Symposium on Fault-Tolerant Systems
Deterministic discrete-event representations of linear continuous-variable systems
Automatica (Journal of IFAC)
State Observation and Diagnosis of Discrete-Event SystemsDescribed by Stochastic Automata
Discrete Event Dynamic Systems
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The increasing importance of fault‐tolerant technical processes leads to new model‐based approaches. In the approach proposed here, the effects of faults are decreased by additional control commands supplied by a fault‐tolerance algorithm. This algorithm combines qualitative model‐based diagnosis and observation. The main idea is to reach the non‐faulty output and non‐faulty state as accurately as possible even in the presence of faults. The signals and faults are described qualitatively by discrete values, and their relations are formalized by an automaton. This automaton is used in diagnosis to identify faults, in observation to identify the present state, and in simulation to identify the correct output and state. A correction mechanism determines an additional control command which would have led to correct behaviour at the current time point and applies it to the process at the following time point. The approach is illustrated by an example of chemical process engineering.