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Classical approaches to the fault detection and isolation usually require extensive plant-modeling and statistical analysis of the measured signals and their residuals versus the developed model. In this paper, alternative simple model-free approach is proposed. Real-time data are preprocessed and self-organizing map is trained and used for the reliable isolation of the most frequent mill fault -...
Paper deals with application of online rotor broken bar fault detection via artificial neural networks. Fault can be detected by monitoring abnormalities of the spectrum amplitudes at certain frequencies in the motor current spectrum. These discriminative features are used for training of feed-forward backpropagation artificial neural network. Trained network is capable to successfully classify induction...
Feed-forward artificial neural networks (ANNs) have been applied to the diagnosis of mixed-mode electronic circuit. In order to tackle the circuit complexity and to reduce the number of test points hierarchical approach to the diagnosis generation was implemented with two levels of decision: the system level and the circuit level. For every level, using the simulation-before-test (SBT) approach, fault...
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