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- W2291093669 abstract "A method is presented for multiple fault diagnosis by means of an Artificial Neural Network (ANN). The major advantage of using an ANN as opposed to any other technique for fault diagnosis in condition ba:-ed maintenance is that the network produces an immediate decision with minimal computation for a given input vector, whereas conventional techniques like spectral analysis require complete processing of an input signal to reach a diagnosis. The basic strategy is to train a neural network to recognize the behavior of the machine condition as well as the behavior of the possible system faults. The multi-layer feed forward network is used in this paper with back propagation learning algorithm. The network is trained by giving training examples, which have known input vector of vibration signatures and output vector of membership of possible faults. Field data of a lubricating oil pump for a residual gas compressor from a LPG recovery plant is used for training and testing the network. For diagnosis purpose, five different states are considered. The correct classification rate during training and testing is very high. On the basis of the results presented it is felt that the application of neural network shows superior performance in fault diagnosis. Transactions on Information and Communications Technologies vol 6, © 1994 WIT Press, www.witpress.com, ISSN 1743-3517" @default.
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- W2291093669 title "Artificial Neural Network Approach ForMultiple Fault Diagnosis: A Case Study" @default.
- W2291093669 doi "https://doi.org/10.2495/ai940081" @default.
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