Recent Advances in Modeling and Online Detection of Stator Interturn Faults in Electrical Motors

TLDR

Online fault diagnosis is essential for fault tolerance in safety‑critical drive systems, where short‑circuit faults are common and motor current is the most frequently analyzed signal. The paper reviews online stator interturn fault detection and diagnosis techniques, with particular emphasis on short‑circuit faults in permanent‑magnet machines and motor‑current signature analysis. The review covers signal‑analysis, model‑based, and knowledge‑based approaches, including parametric and finite‑element models that simulate interturn‑fault conditions.

Abstract

Online fault diagnosis plays a crucial role in providing the required fault tolerance to drive systems used in safety-critical applications. Short-circuit faults are among the common faults occurring in electrical machines. This paper presents a review of existing techniques available for online stator interturn fault detection and diagnosis (FDD) in electrical machines. Special attention is given to short-circuit-fault diagnosis in permanent-magnet machines, which are fast replacing traditional machines in a wide variety of applications. Recent techniques that use signals analysis, models, or knowledge-based systems for FDD are reviewed in this paper. Motor current is the most commonly analyzed signal for fault diagnosis. Hence, motor current signature analysis is a topic of elaborate discussion in this paper. Additionally, parametric and finite-element models that were designed to simulate interturn-fault conditions are reviewed.