Detectability Based Data-Driven Fault Diagnosis Method for Multiple Device Faults of Converters

Abstract

The data-driven fault diagnosis method, which eliminates the need for additional sensors while preserving the system's original structure, offers a promising approach to enhancing converter reliability. However, diagnosing multiple device faults presents significant challenges due to difficulties in fault sample acquisition, detectability uncertainty, and unexplained decisions. This article addresses these challenges by presenting a detectability-based data-driven fault diagnosis method. First, a model-based fault detectability analysis method is proposed to establish the measurement conditions necessary for reliably detecting various fault types. Utilizing these measurement condition constraints, a mechanism-enhanced neural network is designed to locate faults by fitting the changes in fault parameters. The consistency between the fitting fault parameters and the actual fault process ensures the interpretability of the diagnosis results. Additionally, by guaranteeing the identification of fault parameters, the fault circuit model assists in training, significantly reducing the number of actual fault samples required for mechanism-enhanced neural network training. Finally, experiments on a representative converter are conducted to verify the effectiveness of the proposed method. Comparisons with state-of-the-art techniques show the proposed scheme's superiority in terms of diagnostic accuracy, decision explainability, sample dependence.