Self adaptive learning scheme for early diagnosis of simple and multiple switch faults in multicellular power converters

Abstract

This paper proposes a scheme based on the use of unsupervised machine learning approach and a drift detection mechanism in order to perform an early fault diagnosis of simple and multiple stuck-opened/stuck-closed switches in multicellular converters. Only the data samples representing the normal operation conditions are used in order to be adapted to the case where no data is available about faulty behaviors. A health indicator measuring the dissimilarity between normal and current operation conditions is built in order to detect a drift (degradations) in early stage. When a degradation (fault) is detected, the isolation is achieved by taking into account the discrete dynamics of switches. The features related to the latter are extracted in order to build a feature space allowing to separate the faulty behavior (zone or class) of the different switches. The proposed scheme is evaluated using real data samples representing different normal/simple/multiple switch fault scenarios issued from a test rig.