Title: Scalable fault models for diagnosis of synchronous generators

Authors: R. Gopinath; C. Santhosh Kumar; K.I. Ramachandran

Addresses: Machine Intelligence Research Laboratory, Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham University, Coimbatore, 641112, India ' Machine Intelligence Research Laboratory, Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham University, Coimbatore, 641112, India ' Machine Intelligence Research Laboratory, Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham University, Coimbatore, 641112, India

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.

Keywords: fault diagnosis; inter-turn faults; IVHM; integrated vehicle health management; CBM; condition-based maintenance; LLC; locality constrained linear coding; PCA; principal component analysis; scalable fault models; synchronous generators; universal fault diagnosis; machine-independent; support vector machines; SVM.

DOI: 10.1504/IJISTA.2016.076103

International Journal of Intelligent Systems Technologies and Applications, 2016 Vol.15 No.1, pp.35 - 51

Received: 26 Jun 2015
Accepted: 12 Jan 2016

Published online: 24 Apr 2016 *

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