Online Diagnosis of Induction Motors Using MCSA

TLDR

MCSA uses stator‑current signals for motor diagnosis and, while powerful, suffers from shortcomings that degrade performance and accuracy. The paper proposes an online induction motor diagnosis system that employs advanced signal‑and‑data‑processing algorithms to improve MCSA. The system diagnoses four fault types—rotor‑bar and end‑ring breakage, stator‑winding short‑circuit, bearing cracks, and air‑gap eccentricity—using an optimal‑slip‑estimation, proper‑sample‑selection, and frequency‑auto‑search algorithm set. Experimental results on 3.7‑kW and 30‑kW squirrel‑cage induction motors and voltage‑source inverters confirm that the system accurately identifies the four fault types.

Abstract

In this paper, an online induction motor diagnosis system using motor current signature analysis (MCSA) with advanced signal-and-data-processing algorithms is proposed. MCSA is a method for motor diagnosis with stator-current signals. The proposed system diagnoses induction motors having four types of faults such as breakage of rotor bars and end rings, short-circuit of stator windings, bearing cracks, and air-gap eccentricity. Although MCSA is one of the most powerful online methods for diagnosing motor faults, it has some shortcomings, which degrade performance and accuracy of a motor-diagnosis system. Therefore, advanced signal-and-data-processing algorithms are proposed. They are composed of an optimal-slip-estimation algorithm, a proper-sample-selection algorithm, and a frequency auto search algorithm for achieving MCSA efficiently. The proposed system is able to ascertain four kinds of motor faults and diagnose the fault status of an induction motor. Experimental results obtained on 3.7-kW and 30-kW three-phase squirrel-cage induction motors and voltage-source inverters with a vector-control technique are discussed