Publication Type : Journal Article
Publisher : International Journal of Intelligent Systems Technologies and Applications, Inderscience.
Source : International Journal of Intelligent Systems Technologies and Applications, Inderscience (2016)
Url : http://www.inderscienceonline.com/doi/abs/10.1504/IJISTA.2016.076103
Keywords : CBM, condition-based maintenance, Fault diagnosis, integrated vehicle health management, inter-turn faults, IVHM, LLC, locality constrained linear coding, machine-independent, PCA, Principal component analysis, scalable fault models, Support vector machines, SVM, Synchronous generators, universal fault diagnosis
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication, Mechanical Engineering
Year : 2016
Abstract : In this paper, we experiment with a small working model (SWM), where we can inject faults and learn the intelligence about the system, then scale up this fault models to monitor the condition of an actual/complex system, without injecting faults in the actual system. We refer to this approach as scalable fault models. We check the effectiveness of our approach using 3 kVA and 5 kVA synchronous generators to emulate the behaviour of SWM and actual system, respectively. We linearise the features from the SWM and actual system in a higher-dimensional space using locality constrained linear coding (LLC) to make them linearly separable. Subsequently, the system-independent features are selected using principal component analysis (PCA) to make the fault models robust across the systems. Support vector machine (SVM) is used as a back-end classifier. Experiments and results show that proposed LLC-PCA system outperforms the baseline system.
Cite this Research Publication : R. Gopinath, Dr. Santhosh Kumar C., and Dr. K. I. Ramachandran, “Scalable Fault Models for Diagnosis of Synchronous Generators”, International Journal of Intelligent Systems Technologies and Applications, 2016.