Dual features functional support vector machines for fault detection of rechargeable batteries
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
Electric load forecasting based on locally weighted support vector regression
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
| Hi-index | 0.00 |
Timely and accurate information about incipient faults in online machines will greatly enhance the development of optimal maintenance procedures. The application of support vector regression to machine health monitoring was recently investigated; however, such implementation is based on batch processing of the available data. Therefore, the addition of new sample to the already existing dataset requires that the technique should retrain from scratch. This disadvantage makes the technique unsuitable for online systems that will give real-time information to field engineers so that corrective actions could be taken before there is any damage to the system. This paper presents an application of accurate online support vector regression (AOSVR) approach that efficiently updates a trained predictor whenever a new sample is added to the training set using shaft misalignment and nuclear power plant feedwater flow rate data. The results show that the approach is effective for online machine condition monitoring where it is usually difficult to obtain sufficient training data prior to the installation of the online systems.