Scalable fault models for diagnosis of synchronous generators Online publication date: Mon, 25-Apr-2016
by R. Gopinath; C. Santhosh Kumar; K.I. Ramachandran
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 15, No. 1, 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.
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