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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40813

Title: Diagnosis of Mechanical Pumping System Using Neural Networks and System Parameters Analysis
Authors: Tsai, Tai-Ming, Wang, Wei-Hui
Contributors: 國立臺灣海洋大學:輪機工程學系
Keywords: Neural network;System diagnosis;Correlation analysis;Sensitivity analysis;Radial basis function method;Backpropagation method;Adaptive linear method
Date: 2009-07-19
Issue Date: 2017-02-07T01:22:50Z
Publisher: Journal of Mechanical Science and Technology
Abstract: Abstract:Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input-output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended.
Relation: 23, pp.127-138
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40813
Appears in Collections:[輪機工程學系] 期刊論文

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