Fault Detection and Diagnosis of a 3-Phase Induction Motor Using Kohonen Self-Organising Map

Ofosu, Robert Agyare; Odoi, Benjamin; Boateng, Daniel Fosu; Muhia, Asaph Mbugua

doi:10.25077/jnte.v12n1.1047.2023

Cited by 2 publications

(1 citation statement)

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“…The details of the fine-tuned factor and the overall methodology are discussed, offering valuable contributions to the field of motor fault diagnostics. In [14], this study explores the application of the Kohonen Self-Organising Map as a tool for identifying and diagnosing faults in 3-phase induction motors. The paper discussed the methodology, experimental setup, and findings related to the effectiveness of this approach.…”

Section: Literature Reviewmentioning

confidence: 99%

Revolutionizing Motor Health: IoT-Driven Detection of Electrical Abnormalities in Three-Phase A.C. Induction Motors

Alex Kok Bin See,

Jed Jian Yao Ong

2023

Malaysian J. Sci. Adv. Tech.

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This paper presented a comprehensive investigation into the detection of electrical abnormalities in a 3-phase alternating current (AC) induction motor (IM) rated at 1.5 kW under simulated single phasing and overloading test conditions. The findings from data analysis on electrical abnormalities simulated physically on an IM were reported, employing a non-invasive technique. Sensors were deployed to monitor the IM and measurements were taken. The data logging and control were designed using an industrial-grade graphical system design software, LabVIEW, and NI PXIe-1071 embedded controller hardware. A novel combination of in-situ on-line current measurements, infrared temperature detection, and 3-axes micro-electro-mechanical systems (MEMS) accelerometers were utilized for measurements. The paper described an internet of things (IoT) monitoring system capable of alerting the operator of the motor to any electrical abnormalities. Experimental results suggested that an IM subjected to single phasing experienced observable z-plane vibration, with a standard deviation of 0.24 G. For overload tests (i.e., 50 Hz and 30 Hz) on the IM based on the national electrical manufacturers association (NEMA) standard, the percentage of heating ranged between 103.98% and 109.67%, respectively; however, no significant increase in z-plane vibrations was observed.

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Section: Literature Reviewmentioning

confidence: 99%

Revolutionizing Motor Health: IoT-Driven Detection of Electrical Abnormalities in Three-Phase A.C. Induction Motors

Alex Kok Bin See,

Jed Jian Yao Ong

2023

Malaysian J. Sci. Adv. Tech.

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A Comparative Analysis of Support Vector Machine and Decision Tree Algorithm for Predicting Fault in Uninterruptible Power Supply Systems

Doe,

Annan,

Odoi

2024

IJITEE

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Power supply systems can have problems, and Ghana Gas Limited is not an exception. Ghana Gas Limited uses an intricate Uninterruptible Power Supply (UPS) system which is made up of several parts such as electromechanical components, PCB boards, and electrolytic capacitors. The majority of components have technical lifespans that are governed by usage, operational environment, and working conditions, such as electrical stress, working hours, and working cycles. Most of the time, these errors affect the integrity and power supply after manufacture. The issue is that it takes longer for the professionals who operate on this machine to recognize these flaws, which makes it difficult for them to predict errors quickly or anticipate the likelihood of faults happening in the system components at an early stage for effective corrective action to be performed. Support vector machines (SVM) and decision trees were used in this study to anticipate faults for technical data scheduling of uninterruptible power supply systems for Ghana Gas Limited in an efficient manner. Based on a comparative analysis using these two techniques, faults in Ghana Gas Limited's power supply system were predicted using a four-hour daily interval dataset on UPS recordings, including input voltage, battery voltage, battery current, and alarm, spanning from August 2017 to October 2023. The findings depicted that the support vector machine was more efficient in detecting the fault locations in the power supply system with an accuracy of 96.80%, recall of 99.80%, precision of 100 %, F1-score of 93.15%. The results from the error metrics also validate the measures in assessing the predictive ability of the model with MAE of 0.42%, MSE of 1.18%, RMSE of 4.45%, R2 of 99.97%, RMSLE of 0.036%, and MAPE of 0.21%.

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Fault Detection and Diagnosis of a 3-Phase Induction Motor Using Kohonen Self-Organising Map

Cited by 2 publications

References 11 publications

Revolutionizing Motor Health: IoT-Driven Detection of Electrical Abnormalities in Three-Phase A.C. Induction Motors

Revolutionizing Motor Health: IoT-Driven Detection of Electrical Abnormalities in Three-Phase A.C. Induction Motors

A Comparative Analysis of Support Vector Machine and Decision Tree Algorithm for Predicting Fault in Uninterruptible Power Supply Systems

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