Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Expert Systems with Applications: An International Journal
Estimation and optimisation of right-censored data in survival analysis by neural network
International Journal of Business Information Systems
| Hi-index | 12.05 |
Condition monitoring (CM) of machines health or industrial components and systems that can detect, classify and predict the impending faults is critical in reducing operating and maintenance cost. Many papers have reported the valuable models and methods of prognostic systems. However, it was rarely found the papers deal with censored data, which was common in machine condition monitoring practice. This work deals with development of machine degradation assessment system that utilizes censored and complete data collected from CM routine. Relevance vector machine (RVM) is selected as intelligent system then trained by input data obtained from run-to-failure bearing data and target vectors of survival probability estimated by Kaplan-Meier (KM) and probability density function estimators. After validation process, RVM is employed to predict survival probability of individual unit of machine component. The plausibility of the proposed method is shown by applying the proposed method to bearing degradation data in predicting survival probability of individual unit.