International Journal of Man-Machine Studies
The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A decision based one-against-one method for multi-class support vector machine
Pattern Analysis & Applications
HYDES: A Web-based hydro turbine fault diagnosis system
Expert Systems with Applications: An International Journal
Rough set based 1-v-1 and 1-v-r approaches to support vector machine multi-classification
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Improved Classification for Problem Involving Overlapping Patterns
IEICE - Transactions on Information and Systems
Reducing the storage requirements of 1-v-1 support vector machine multi-classifiers
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Global geometric similarity scheme for feature selection in fault diagnosis
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The fault diagnosis for hydroelectric generator unit (HGU) is significant to prevent dangerous accidents from occurring and to improve economic efficiency. The faults of HGU involve overlapping fault patterns which may denote a kind of faults in the early stage or a subset of samples that caused by multi-fault. But until now it has not been considered in the traditional classifier of fault diagnosis for HGU. In this paper, a novel classifier combined rough sets and support vector machine is proposed and applied in the fault diagnosis for HGU. Instead of classifying the patterns directly, the fault patterns lying in the overlapped region are extracted firstly. Then, upper and lower approximations of each class are defined on the basis of rough set technique. Next, for the fault patterns lying in the overlapped region, the reliability they belong to a certain class is calculated. At last, the proposed method is successfully applied in analyzing an international standard data set, as well as diagnosing the vibrant faults of a HGU. The results show that the proposed classifier can more properly describe the complex map between the faults and their symptoms, and is suitable to fault diagnosis for HGU.