Hierarchical Bayesian learning for electrical transient classification

This paper addresses the problem of the supervised signal classification, by using a hierarchical Bayesian method. Each signal is characterized by a set of parameters, the features, which are estimated from a set of learning signals. Moreover, these parameters are distributed according to a class-specific posterior distribution which allows one to capture the variability of the features within the same class. Within the hierarchical Bayesian framework, the feature extraction step and the learning step can be performed jointly. Unfortunately, the estimation of the class-specific distribution parameters requires the computation of intractable multi-dimensional integrals. Then a Markov-chain Monte Carlo (MCMC) algorithm is used to sample the posterior distributions of the features over all the training signals of each class. An application to electrical transient classification for non-intrusive load monitoring is introduced. Simulations over real-world electrical transients signals are driven and show the capacity of the proposed methodology to discriminate two classes of transients.