contributor author | B. Samanta | |
contributor author | C. Nataraj | |
date accessioned | 2017-05-09T00:32:00Z | |
date available | 2017-05-09T00:32:00Z | |
date copyright | December, 2009 | |
date issued | 2009 | |
identifier issn | 1530-9827 | |
identifier other | JCISB6-26008#044502_1.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/140115 | |
description abstract | A study is presented on applications of computational intelligence (CI) techniques for monitoring and prognostics of machinery conditions. The machine condition is assessed through an energy-based feature, termed as “energy index,” extracted from the vibration signals. The progression of the “monitoring index” is predicted using the CI techniques, namely, recursive neural network (RNN), adaptive neurofuzzy inference system (ANFIS), and support vector regression (SVR). The proposed procedures have been evaluated through benchmark data sets for one-step-ahead prediction. The prognostic effectiveness of the techniques has been illustrated through vibration data set of a helicopter drivetrain system gearbox. The prediction performance of SVR was better than RNN and ANFIS. The improved performance of SVR can be attributed to its inherently better generalization capability. The training time of SVR was substantially higher than RNN and ANFIS. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating feature, the level of damage or degradation, and their progression. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Prognostics of Machine Condition Using Energy Based Monitoring Index and Computational Intelligence | |
type | Journal Paper | |
journal volume | 9 | |
journal issue | 4 | |
journal title | Journal of Computing and Information Science in Engineering | |
identifier doi | 10.1115/1.3249574 | |
journal fristpage | 44502 | |
identifier eissn | 1530-9827 | |
keywords | Machinery | |
keywords | Mechanical drives | |
keywords | Vibration | |
keywords | Signals | |
keywords | Time series AND Gears | |
tree | Journal of Computing and Information Science in Engineering:;2009:;volume( 009 ):;issue: 004 | |
contenttype | Fulltext | |