Title :
Research of equipment reliability prognosis model based on SVR
Author :
Sun, Lei ; Jia, Yunxian
Author_Institution :
Dept. Equip. Command & Manage., Ordnance Eng. Coll., Shijiazhuang, China
Abstract :
Reliability prognostics plays a serious role in maintenance management of machine, the ability to forecast machinery failure is vital to reducing maintenance cost, operation downtime or operation risk. This paper presents a novel approach for machinery reliability prognostics via incorporating health degradation data of units and Support Vector Regression (SVR). The proposed model was used to forecast the reliability and failure time of the diesel motor.
Keywords :
cost reduction; failure (mechanical); forecasting theory; machinery; maintenance engineering; mechanical engineering computing; regression analysis; reliability; risk management; support vector machines; diesel motor; equipment reliability prognosis model; health degradation data; machine maintenance management; machinery failure forecasting; machinery reliability prognostics; maintenance cost reduction; operation downtime reduction; operation risk reduction; support vector regression; Data models; Degradation; Iron; Mathematical model; Predictive models; Reliability; Support vector machines; health degradation; prognostics; reliability; support vector regression;
Conference_Titel :
Quality, Reliability, Risk, Maintenance, and Safety Engineering (ICQR2MSE), 2011 International Conference on
Conference_Location :
Xi´an
Print_ISBN :
978-1-4577-1229-6
DOI :
10.1109/ICQR2MSE.2011.5976669
