Early warning of slight changes in systems
Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to variable and feature selection
The Journal of Machine Learning Research
Fault prognostics using dynamic wavelet neural networks
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Assessment of the effectiveness of support vector machines for hyperspectral data
Future Generation Computer Systems - Special issue: Geocomputation
Intelligent prognostics tools and e-maintenance
Computers in Industry - Special issue: E-maintenance
Similarity based method for manufacturing process performance prediction and diagnosis
Computers in Industry
An overview of statistical learning theory
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
| Hi-index | 12.05 |
In condition-based maintenance (CBM), effective diagnostic and prognostic tools are essential for maintenance engineers to identify imminent fault and predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedule of production if necessary. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of bearings based on health state probability estimation and historical knowledge embedded in the closed loop diagnostics and prognostics system. The technique uses the Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation process to provide long term prediction. To validate the feasibility of the proposed model, real life fault historical data from bearings of High Pressure-Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life (RUL). The results obtained were very encouraging and showed that the proposed prognosis system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.