Soft Computing Approaches to Fault Diagnosis for Dynamic Systems

@article{Calado2001SoftCA,
  title={Soft Computing Approaches to Fault Diagnosis for Dynamic Systems},
  author={Jo{\~a}o M. Ferreira Calado and J{\'o}zef Korbicz and Krzysztof Patan and Ron John Patton and Jos{\'e} S{\'a} da Costa},
  journal={Eur. J. Control},
  year={2001},
  volume={7},
  pages={248-286}
}
Recent approaches to fault detection and isolation for dynamic systems using methods of integrating quantitative and qualitative model information, based upon soft computing (SC) methods are surveyed and studied in some detail. SC methods are considered an important extension to the quantitative model-based approach for residual generation in fault detection and isolation (FDI). When quantitative models are not readily available, a correctly trained neural network (NN) can be used as a non… 
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References

SHOWING 1-10 OF 165 REFERENCES
Issues of Fault Diagnosis for Dynamic Systems
There is an increasing demand for dynamic systems to become safer, more reliable and more economical in operation. This requirement extends beyond the normally accepted safety-critical systems e.g.,…
Analytical and Qualitative Model-based Fault Diagnosis - A Survey and Some New Results
  • P. Frank
  • Computer Science
    Eur. J. Control
  • 1996
Application of a hybrid neuro-fuzzy system to the fault diagnosis of an automotive electromechanical actuator
Robust residual generation for model-based fault diagnosis of dynamic systems.
TLDR
This thesis proposes a new full-order unknown input observer structure for robust residual generation and this structure is then used to design directional and minimum variance residuals and studies the design of optimally robust parity relations using multi-criterion optimization.
An Approach to Fault Diagnosis for Non Linear Dynamic Systems Using Neural Networks
A Fuzzy FDI Decision Making System for the Support of the Human Operator
Fault diagnosis of machines via parameter estimation and knowledge processing - Tutorial paper
Robustness in Model-Based Fault Diagnosis: The 1995 Situation
Application of Supervised and Unsupervised Learning Methods to Fault Diagnosis
An Intelligent On-Line Supervisory Fault Detection and Diagnosis System
...
...