Data-Driven Gap Metric Fault Diagnosis Technology With Its Application to DC-DC Converter

Abstract

This paper investigates a data-driven gap metric fault detection and isolation method for buck DC-DC converters with component faults. First, the averaged state space model of a buck DC-DC converter and its component (inductor, capacitor and load resistance) fault models are established. Second, a data-driven gap metric using subspace identification is utilized to detect the occurred component faults. Third, to isolate these faults, the concept of fault cluster is firstly developed and then the definition of fault isolation under gap matric is proposed. Based on it, a fault isolation condition is presented by solving its fault cluster center model and radius. Finally, the simulation and experiment are reported to demonstrate the effectiveness of the used method.