Abstract
Abstract In this paper a complex drive chain is modelled with Local Linear Neuro-Fuzzy Model (LLNF). The developed models were used for detecting different faults that may occur in the system. The models were developed based on measurements carried out on the real system. Using feed-forward neural networks with perceptron neuron structure, model-based fault diagnosis of the analysed system was developed to separate the different faults. The performance and efficiency of the developed different types of artificial neural network's structures were compared using gradient based edge detection method.
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