A Data-Driven Fault Diagnosis Methodology in Three-Phase Inverters for PMSM Drive Systems

TLDR: In this paper, a Bayesian network-based data-driven fault diagnosis methodology of three-phase inverters is proposed to solve the uncertainty problem in fault diagnosis of inverters, which is caused by various reasons, such as bias and noise of sensors.

Abstract: Permanent magnet synchronous motor and power electronics-based three-phase inverter are the major components in the modern industrial electric drive system, such as electrical actuators in an all-electric subsea Christmas tree. Inverters are the weakest components in the drive system, and power switches are the most vulnerable components in inverters. Fault detection and diagnosis of inverters are extremely necessary for improving drive system reliability. Motivated by solving the uncertainty problem in fault diagnosis of inverters, which is caused by various reasons, such as bias and noise of sensors, this paper proposes a Bayesian network-based data-driven fault diagnosis methodology of three-phase inverters. Two output line-to-line voltages for different fault modes are measured, the signal features are extracted using fast Fourier transform, the dimensions of samples are reduced using principal component analysis, and the faults are detected and diagnosed using Bayesian networks. Simulated and experimental data are used to train the fault diagnosis model, as well as validate the proposed fault diagnosis methodology.