A review of intelligent methods for condition monitoring and fault diagnosis of stator and rotor faults of induction machines

Abstract

Nowadays, induction motor (IM) is extensively used in industry, including mechanical and electrical applications. However, three main types of IM faults have been discussed in the literature, bearing, stator, and rotor. Importantly, stator and rotor faults represent approximately 50%. Traditional condition monitoring (CM) and fault diagnosis (FD) methods require a high processing cost and much experience knowledge. To tackle this challenge, artificial intelligent (AI) based CM and FD techniques are extensively developed. However, there have been many review research papers for intelligent CM and FD machine learning methods of rolling elements bearings of IM in the literature. Whereas there is a lack in the literature, and there are not many review papers for both stator and rotor intelligent CM and FD. Thus, the proposed study's main contribution is in reviewing the CM and FD of IM, especially for the stator and the rotor, based on AI methods. The paper also provides discussions on the main challenges and possible future works.

An improved incipient whale optimization algorithm based robust fault detection and diagnosis for sensorless brushless DC motor drive under external disturbances

“…It has several advantages, including excellent speed‐torque characteristics, decent static and dynamic characteristics, high performance, higher speed ranges up to 10 000 rpm, long service life, and quiet operation. The stator winding of the BLDC motor is made of stacked laminated steel 22 . There are two types of connections: star relation and delta.…”

“…The stator winding of the BLDC motor is made of stacked laminated steel. 22 There are two types of connections: star relation and delta. The star connection provides high starting torque at slow speeds, while the delta connection provides low starting torques at slow speeds.…”

“…It covers the desired target tightly with a massive spider-knotted web and makes the desired target toward the middle of the coverage location. 22,23 The optimal search as shown in Equations ( 17) and ( 18)…”

“…It is noted that humpback whale able to migrate up a coiled track at a depth of about 15 m with the diverse size of bubbles. It covers the desired target tightly with a massive spider‐knotted web and makes the desired target toward the middle of the coverage location 22,23 …”

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“…Hence, precise fault detection and localization can be accomplished by a sensible review of the trends and variations in stator current, voltage, and electromagnetic field. Various techniques have been presented in the literature for detecting, localizing, and isolating faults in electric drives [1][2][3].…”