Reconfigurable Distributed Control
Reconfigurable Distributed Control
Issues of Fault Diagnosis for Dynamic Systems
Issues of Fault Diagnosis for Dynamic Systems
Observations and problems applying ART2 for dynamic sensor pattern interpretation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Fault diagnosis currently offers different alternatives to classify faults at early stages, such as model-based and knowledge-based techniques. Nevertheless, fault classification for time-varying systems is still an open problem. Strategies such as self-organizing maps and principal component analysis ensure fault classification to bounded time-variance faults. The approach presented in this paper proposes the use of three non-supervised neural networks. The first two networks overlapped by certain time shift. The third network performs a comparison between the two networks outputs in the previous stage. As a result, the system classifies the fault even if the system is time-variant. The strategy named as Overlapped ART2A Network, aims to obtain an autonomous performance and on-line fault classification. Results show the effectiveness of the approach considering a case study with fault and fault-free scenarios.