Testing and Analysis Fault of Induction Motor for Case Study Misalignment Installation Using Current Signal with Energy Coefficient

Prainetr, Supachai; Tunyasrirut, Satean; Wangnipparnto, Santi

doi:10.3390/wevj12010037

Cited by 6 publications

(5 citation statements)

References 21 publications

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“…The misalignment can be parallel, angular or both (combined: parallel and angular). It is very difficult to obtain a perfect alignment between two shafts in industrial applications [28,31]. Even if a precise alignment is ensured, it cannot be maintained for a long time due to many external effects such as a disturbance of the base foundation.…”

Section: Faults Descriptionmentioning

confidence: 99%

Induction motor mechanical defect diagnosis using DWT under different loading levels

Bouzida¹,

Abdelli²,

Boudouda³

2023

Acta Polytech

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The information extraction capability of the widely used signal processing tool, FFT for diagnosing induction machines, is commonly used at a constant load or at different levels. The loading level is a major influencing factor in the diagnostic process when the coupled load and the machine come with natural mechanical imperfections, and at a low load, the mechanical faults harmonics are strongly influenced. In this context, the main objective of this work is the detection of the mechanical faults and the study of the effect of the loading level on the induction motor diagnostic process. We have employed a diagnosis method based on discrete wavelet transform (DWT) for the multi-level decomposition of stator current and extracting the fault’s energy stored over a wide frequency range. The proposed approach has been experimentally tested on a faulty machine with dynamic eccentricity and a shaft misalignment for three loading levels. The proposed method is experimentally tested and the results are provided to verify the effectiveness of the fault detection and to point out the importance of the coupled load.

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Section: Faults Descriptionmentioning

confidence: 99%

Induction motor mechanical defect diagnosis using DWT under different loading levels

Bouzida¹,

Abdelli²,

Boudouda³

2023

Acta Polytech

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“…Work [10] categorizes malfunctions of the induction motor with a squirrel-cage rotor. It is shown that the largest number of failures relate to damage to the stator of the engine, but non-analyzed questions remained regarding the causes of failures in the stator of the induction motor.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Determining an additional diagnostic parameter for improving the accuracy of assessment of the condition of stator windings in an induction motor

Gubarevych

Goolak

Дакі

et al. 2021

EEJET

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This paper has proposed and substantiated the application of an additional diagnostic parameter for assessing the state of stator windings of induction motors during operation. The dependences of the values of phase shifts between phase currents and phase voltages have been obtained. These dependences showed that when an inter-turn short circuit occurs in the stator windings, the phase shifts are the same for all phases of the motor. That has made it possible to obtain the dependence of the change in phase shift on the change in the engine shaft rotation frequency. This study's result has established the dependence of the rates of change of the phase angle on the engine shaft rotation frequency for both one and two damaged phases with varying degrees of damage. When analyzing these dependences, it was found that with an increase in the number of damaged phases of the electric motor, the linear section of the dependences decreases. In addition, with an increase in the degree of phase damage, the angle of inclination of the linear sections of the characteristics decreases. That has made it possible to determine an additional parameter for diagnosing the place and degree of an inter-turn short circuit of the windings in an induction motor with a squirrel-cage rotor. The values of the additional parameter, termed by this paper's authors as a "phase criterion" can be used to assess the condition and degree of damage to the stator winding of induction motors. The values of the phase criteria for various types of damage were: when phase A is damaged by 90 %, ξ=0.634, (deg)2/(rpm)2; when phase A is damaged by 80 %, ξ=0.393, (deg)2/(rpm)2; when phase A is damaged by 80 % and phase B is damaged by 90 %, ξ=0.25, (deg)2/(rpm)2; when phase A is damaged by 80 % and phase B is damaged by 90 %, ξ=0.173, (deg)2/(rpm)2. The results of this research could be used to select an effective method for diagnosing an inter-turn short circuit in the stator winding when building a diagnostic system for induction motors as part of drives of transport equipment

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“…The profitability of any manufacturing facility is extremely reliant on the time machines are operational. In order to increase the company's profit margin, it is crucial to decrease outages, as maintenance expenses account for approximately 15-20% of total production costs [1]. It is a fact, however, that nearly 30% of maintenance expenditures are a waste of money due to improper maintenance strategy implementation and failure to conduct maintenance at the appropriate times.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Bearing Fault Diagnosis Using Transfer Learning and Random Forest Classification: A Comparative Study on Variable Working Conditions

Saha,

Hoque,

Chowdhury

2024

IEEE Access

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Rotating machines require bearings to operate smoothly. However, wear, misalignment, and poor lubrication can degrade bearings over time. Fault diagnosis models identify and classify bearing faults. A fault diagnosis model trained in a specific working condition may not perform well in different working conditions. Real-world datasets are mixed with various work environment conditions; therefore, validating a model using different working conditions datasets is necessary. In this study, raw vibrational accelerometer data of variable working conditions is preprocessed using the window length and stride method to generate a data format suitable for evaluating the proposed model. This model employs the Transfer learning-based VGG16 model as the feature extractor and random forest as the classifier, and it has proven to be highly effective. This proposed fault diagnosis model adapts to different work environments and enhances fault classification at variable working conditions. The performance of the proposed model is evaluated using various metrics such as confusion matrix heatmap, t-SNE plot, precision-recall curve and learning curve. Results obtained from these metrics indicate that this model performs well compared to others. The overall accuracy of the model is 99.90%, and both the training and testing of this model are fast. It is evident from the learning curve evaluation that this model is free from over-or under-fitting issues. Overall, this model is reliable and suitable for classifying bearing faults at different working conditions and can be useable for real world purposes.INDEX TERMS Bearing fault, bearing fault classification, bearing fault classification under variable working conditions, machine condition monitoring, random forest, transfer learning, vgg16.

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Testing and Analysis Fault of Induction Motor for Case Study Misalignment Installation Using Current Signal with Energy Coefficient

Cited by 6 publications

References 21 publications

Induction motor mechanical defect diagnosis using DWT under different loading levels

Induction motor mechanical defect diagnosis using DWT under different loading levels

Determining an additional diagnostic parameter for improving the accuracy of assessment of the condition of stator windings in an induction motor

Enhancing Bearing Fault Diagnosis Using Transfer Learning and Random Forest Classification: A Comparative Study on Variable Working Conditions

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