An Unsupervised Automated Method to Diagnose Industrial Motors Faults

Sheikh, Muhammad Arrian; Saad, Nordin; Nor, Nursvarizal bin Mohd; Rakhsh, Sheikh Tahir; Irfan, Muhammad

doi:10.1109/esars-itec.2018.8607646

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Electrical machines are considered as one of the main components of the engineering system and are widely used in industries [1][2][3]. Among all the electrical machines, the induction motor (IM) is extensively employed in industries due to several features like its reliability, robustness, simplicity, low maintenance [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Detection and Classification of Phase Failure Faults for a Three-Phase Induction Motor Using Algorithm Based on Rules Derived From Simulation of These Faults

Dawood,

Hasaneen,

Mahmoud

2024

Journal of Al-Azhar University Engineering Sector

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A three-phase induction motor (IM) is one of the most widely used motors in various industrial and commercial applications due to its reliability, robustness, simplicity, low maintenance...etc. Despite its many features, it is exposed to many faults that affect its performance and may cause damage and burning of its coils. Among these faults is opening of phase during its operation which leads to an increase in the temperature of its windings. In this research, a highly efficient algorithm is designed to detect and classify 19 different types of open phase faults. This algorithm is based on rules or conditions derived from simulating the different types of open phase faults in three motor modes (steady-state, transient, and standstill modes). The proposed algorithm uses the per-unit values of the phase voltages, line currents and speed as well of the motor and thus can be applied to different motors. The proposed algorithm has been designed and tested using the simulink window in MATLAB software. All test results proved that the proposed algorithm works very efficiently and is characterized by its speed and high accuracy in detecting these faults. Therefore, it can be used and integrated within the main protection system of a 3-phase IM to detect and classify the various open phase faults.

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Section: Introductionmentioning

confidence: 99%

Detection and Classification of Phase Failure Faults for a Three-Phase Induction Motor Using Algorithm Based on Rules Derived From Simulation of These Faults

Dawood,

Hasaneen,

Mahmoud

2024

Journal of Al-Azhar University Engineering Sector

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“…A survey of different diagnosis methods for various faults type in induction motors was introduced by [13]. In [14] ANN is used to diagnosis and segregate faults in induction motors for improving the traditional methods since it is better solution to statistical analyzing. In [15] was proposed a new method of asymmetry detection in stator winding and localization of faults in PMSM controlled via Field-Oriented control.…”

Section: Introductionmentioning

confidence: 99%

Faults diagnosis in stator windings of high speed solid rotor induction motors using fuzzy neural network

Radhi

Zayer

2021

IJPEDS

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The paper deals with faults diagnosis method proposed to detect the inter-turn and turn to earth short circuit in stator winding of three-phase high-speed solid rotor induction motors. This method based on negative sequence current of motor and fuzzy neural network algorithm. On the basis of analysis of 2-D electromagnet field in the solid rotor the rotor impedance has been derived to develop the solid rotor induction motor equivalent circuit. The motor equivalent circuit is simulated by MATLAB software to study and record the data for training and testing the proposed diagnosis method. The numerical results of proposed approach are evaluated using simulation of a three-phase high-speed solid-rotor induction motor of two-pole, 140 Hz. The results of simulation shows that the proposed diagnosis method is fast and efficient for detecting inter-turn and turn to earth faults in stator winding of high-speed solid-rotor induction motors with different faults conditions

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Artificial intelligence application in fault diagnostics of rotating industrial machines: a state-of-the-art review

et al. 2021

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An Unsupervised Automated Method to Diagnose Industrial Motors Faults

Cited by 4 publications

References 16 publications

Detection and Classification of Phase Failure Faults for a Three-Phase Induction Motor Using Algorithm Based on Rules Derived From Simulation of These Faults

Detection and Classification of Phase Failure Faults for a Three-Phase Induction Motor Using Algorithm Based on Rules Derived From Simulation of These Faults

Faults diagnosis in stator windings of high speed solid rotor induction motors using fuzzy neural network

Artificial intelligence application in fault diagnostics of rotating industrial machines: a state-of-the-art review

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