Condition monitoring and severity estimation of rotor demagnetisation fault using magnetic flux measurement data

Abstract

This article studies and investigate the magnetic characteristics of a surface mounted permanent magnet (SPM)-type brushless direct current (BLDC) motor when subjected to demagnetisation fault conditions. The proposed research subjugates the limitation of estimating the State of Health (SoH) of a BLDC motor drive when deployed in electric vehicle (EV) applications. Demagnetisation faults adversely affect the performance of a machine and indirectly the EV drive system, bringing significant transformation in the machine's characteristics and quantities. The novel method of measuring the radial magnetic field (B g ) across the machine airgap assists in diagnosing and estimating the % severity of faults under the subjected fault conditions. A numerical co-simulation-based model of a BLDC motor is developed to investigate the performance of the machine under both healthy and fault conditions. The BLDC motor being studied is operated using a field programmable gate array (FPGA) based control drive adopting a hysteresis current control (HCC) technique. The experimental investigation of a complete BLDC motor test drive is carried out with an FPGA-based algorithm developed on the Xilinx ISE tool. The outcomes obtained are validated with the numerical results obtained through a finite element (FE) analysis. The proposed study uses a fluxgate sensor for experimental measurement of the flux density of a BLDC motor drive, which is vital for estimating the fault severity and scheduling the maintenance accordingly. The experimental investigations are found to be in validation with the proposed techniques in estimating the health of a BLDC motor-driven EV drive system.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

A novel multi‐strategy self‐optimizing SAPSO algorithm for PMSM parameter identification

“…Unfortunately, the IPMSM is a strongly coupled, multivariable, and non-linear system whose parameters are susceptible to external influences [4], for example, stator resistance is perturbed due to skin effect caused by temperature and frequency, inductance and magnetic flux deviations due to current and magnetic saturation, resulting in a reduction in controller performance. In addition, the control system becomes unstable or even leads to major safety incidents due to demagnetisation fault [5]. In other words, magnetic flux provides more reliable and informative information for fault diagnosis.…”