期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2012
卷号：2
期号：4
页码：166-175
出版社：International Journal of Soft Computing & Engineering
摘要：In this study the Feed Forward Artificial Neural Networks (FFANN) for crack identification and estimates the turbine operating conditions in Francis turbine type was investigated. The sets of vibration data were used as vibrational signatures for studied mechanical structure, and they fed to FFANN as input vector for identification purpose. Different arrangements of FFANN were taken into consideration to find out the best topology which can produce identification results with acceptable accuracy levels. In order to examine the performance of the FFANN and obtain the satisfactory arrangements, different numbers of input data sets are tested. The test results showed that the use of very large number of input data will cause a large increase in training time beside to it may lead to unstable FFANN with over-fitting. To avoid these deteriorated results, different data reduction techniques have been proposed for reducing dimensionality of the input data to achieve an acceptable data reduction level. The conducted results indicated that the FFANN models have been successfully employed for crack identification and estimates the turbine operating conditions using vibration data sets. Moreover the results revealed that the pruning mechanism which is based on the data reduction mechanism can led to satisfactory results.
