Classification of EMI discharge sources using time-frequency features and multi-class support vector machine

This paper introduces the first application of feature extraction and machine learning to electromagnetic interference (EMI) signals for discharge sources classification in high voltage power generating plants. This work presents an investigation on signals that represent different discharge sources, which are measured using EMI techniques from operating electrical machines within power plant. The analysis involves time-frequency image calculation of EMI signals using general linear chirplet analysis (GLCT) which reveals both time and frequency varying characteristics. Histograms of uniform local binary patterns (LBP) are implemented as a feature reduction and extraction technique for the classification of discharge sources using multi-class support vector machine (MCSVM). The novelty that this paper introduces is the combination of GLCT and LBP applications to develop a new feature extraction algorithm applied to EMI signals classification. The proposed algorithm is demonstrated to be successful with excellent classification accuracy being achieved. For the first time, this work transfers expert's knowledge on EMI faults to an intelligent system which could potentially be exploited to develop an automatic condition monitoring system.