TY - GEN
T1 - Prognostics of machine condition using energy based monitoring index and computational intelligence
AU - Samanta, B.
AU - Nataraj, C.
PY - 2009
Y1 - 2009
N2 - A procedure is presented for monitoring and prognostics of machine conditions using computational intelligence (Cl) techniques. 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 Cl techniques, namely, recursive neural network (RNN), adaptive neuro-fuzzy inference system (ANFIS) and support vector regression (SVR). The proposed prediction procedures have been evaluated through benchmark datasets. The prognostic effectiveness of the techniques has been illustrated through vibration dataset of a helicopter drivetrain system gearbox. The performance of SVR was found to be better than RNN and ANFIS for the dataset used. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating feature, the level of damage/degradation and their progression.
AB - A procedure is presented for monitoring and prognostics of machine conditions using computational intelligence (Cl) techniques. 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 Cl techniques, namely, recursive neural network (RNN), adaptive neuro-fuzzy inference system (ANFIS) and support vector regression (SVR). The proposed prediction procedures have been evaluated through benchmark datasets. The prognostic effectiveness of the techniques has been illustrated through vibration dataset of a helicopter drivetrain system gearbox. The performance of SVR was found to be better than RNN and ANFIS for the dataset used. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating feature, the level of damage/degradation and their progression.
KW - Computational intelligence
KW - Energy index
KW - Machine fault prognostics
KW - Neuro-fuzzy systems
KW - Support vector regression
UR - http://www.scopus.com/inward/record.url?scp=70349243569&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:70349243569
SN - 9780791843253
T3 - 2008 Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC 2008
SP - 1347
EP - 1358
BT - 2008 Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC 2008
T2 - 2008 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, DETC 2008
Y2 - 3 August 2008 through 6 August 2008
ER -