Automatica (Journal of IFAC)
Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Model-Based Fault Diagnosis in Dynamic Systems Using Identification Techniques
Model-Based Fault Diagnosis in Dynamic Systems Using Identification Techniques
Ant colony optimization theory: a survey
Theoretical Computer Science
New nonlinear residual feedback observer for fault diagnosis in nonlinear systems
Automatica (Journal of IFAC)
Engineering Applications of Artificial Intelligence
Use of particle swarm optimization for machinery fault detection
Engineering Applications of Artificial Intelligence
Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems: From Analytical to Soft Computing Approaches
Issues of Fault Diagnosis for Dynamic Systems
Issues of Fault Diagnosis for Dynamic Systems
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Process fault detection based on modeling and estimation methods-A survey
Automatica (Journal of IFAC)
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This paper proposes an approach for Fault Diagnosis and Isolation (FDI) on industrial systems via faults estimation. FDI is presented as an optimization problem and it is solved with Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms. Also, is presented a study of the influence of some parameters from PSO and ACO in the desirable characteristics of FDI, i.e. robustness and sensitivity. As a consequence, the Particle Swarm Optimization with Memory (PSO-M) algorithm, a new variant of PSO was developed. PSO-M has the objective of reducing the number of iterations/generations that PSO needs to execute in order to provide a reasonable quality diagnosis. The proposed approach is tested using simulated data from a DC Motor benchmark. The results and analysis indicate the suitability of the approach as well as the PSO-M algorithm.