A novel indicator of stator winding inter-turn fault in induction motor using infrared thermal imaging

Eftekhari, M.; Moallem, Mehdi; Sadri, Saeed; Hsieh, Min‐Fu

doi:10.1016/j.infrared.2013.10.001

Cited by 48 publications

(16 citation statements)

References 10 publications

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“…Asenkron motorlarda en çok karşılaşılan arıza türlerinden bir diğeri de motor stator hatalarıdır. Stator hataları genellikle, stator sargılarında, sargı laminasyon malzemelerinde, statorun gövdesini oluşturan yapıda meydana gelmektedir (Eftekhari et al, 2013). Stator üzerinde ayrıca elektriksel olarak gövdeye kısa devre arızası, açık devre arızası, fazdan faza kısa devre, sargıdan sargıya kısa devre gibi arızalar da meydana gelmektedir (Mousavi et al, 2017).…”

Section: Stator Arızalarıunclassified

Bir Asenkron Motorun Mekanik Titreşim Sinyallerinin Ölçülerek Arıza Analizinin Yapılması

Çeven¹,

Bayır²

2020

European Journal of Science and Technology

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Öz Asenkron motorlar, endüstride en yaygın kullanılan motor türüdür. Asenkron motorların verimlerinde meydana gelen bozulmalar, çalışma performanslarını ciddi şekilde etkilemektedir. Üretim sürecinin aksamaması için bu motorların bakımlarının periyodik bir şekilde yapılması ve durumlarının izlenmesi gerekmektedir. Bu çalışmada bir asenkron motorun farklı hız kademelerinde mekanik titreşimlerinin ölçülmesi için bir deney düzeneği tasarlanmış ve gerçekleştirilmiştir. Deney düzeneğinde asenkron motorun gövdesine "Z" ekseni yönünde bağlanan bir ivmeölçer yardımıyla titreşim sinyalleri elektrik sinyaline dönüştürülmüş ve bir veri alışveriş kartı üzerinden bilgisayar ortamına aktarılmıştır. Bu yöntem ile asenkron motorda gerçek zamanlı durum izleme gerçekleştirilmektedir. Motor üzerinde oluşan mekanik titreşimler ayrıca titreşim ölçümü yapabilen test cihazı ile ölçülmüş ve hafıza kartına kayıt edilmiştir. Elde edilen sensör verileri FFT dönüşümü kullanılarak analiz edilmiş ve sonuçlar yorumlanmıştır.

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Section: Stator Arızalarıunclassified

Bir Asenkron Motorun Mekanik Titreşim Sinyallerinin Ölçülerek Arıza Analizinin Yapılması

Çeven¹,

Bayır²

2020

European Journal of Science and Technology

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“…However, most of the works aim to detect simple failures, often external to the machine. Some recent works have proposed its extension to the detection of other faults [15], [16] showing interesting conclusions. These works prove that the images obtained with infrared thermography can be helpful to detect hot spots or areas that can provide an alarm of an incorrect operation of the motor or any of its constituting elements [15].…”

Section: Introductionmentioning

confidence: 98%

An automated thermographic image segmentation method for induction motor fault diagnosis

Karvelis

Georgoulas

Stylios

et al. 2014

IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society

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Eventual failures in induction machines may lead to catastrophic consequences in terms of economic costs for the companies. The development of reliable systems for fault detection that enable to diagnose a wide range of faults is a motivation of many researchers worldwide. In this context, non-invasive condition monitoring strategies have drawn special attention since they do not require interfering with the operation process of the machine. Though the analysis of the motor currents has proven to be a reliable, non-invasive methodology to detect some of the faults (especially when assessing the rotor condition), it lacks reliability for the diagnosis of other faults (e.g. bearing faults). The infrared thermography has proven to be an excellent, non-invasive tool that can complement the diagnosis reached with the motor current analysis, especially for some specific faults. However, there are still some pending issues regarding its application to induction motor faults diagnosis, such as the lack of automation or the extraction of reliable fault indicators based on the infrared data. This paper proposes a methodology that intends to provide a solution to the first issue: a method based on image segmentation is employed to detect several failures in an automated way. Four specific faults are analyzed: bearing faults, fan failures, rotor bar breakages and stator unbalance. The results show the potential of the technique to automatically identify the fault present in the machine.

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“…In the literature, different types of signals have been used as an indicator for motor fault diagnosis. These indicators include magnetic flux, vibration, stator current (negative and zero sequence or third harmonic component), zero sequence component of voltage, thermal image processing, instantaneous real power and reactive power, phase shift between stator and supply voltages, and acoustic noise [9,11,[15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Moreover, many researchers used the instantaneous torque as a fault indicator in induction motors [18,21,[29][30][31].…”

Section: Introductionmentioning

confidence: 99%

“…A novel technique has been developed for online detection of stator inter-turns faults in the induction machine using infrared images [16]. Hence, the magnitude of the current in the faulty part of the stator winding was significantly higher than the rated current of the machine.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors

2018

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Induction motors constitute the largest proportion of motors in industry. This type of motor experiences different types of failures, such as broken bars, eccentricity, and inter-turn failure. Stator winding faults account for approximately 36% of these failures. As such, condition monitoring is used to protect motors from sudden breakdowns. This paper proposes the use of neural networks as an efficient diagnostic tool for estimating the percentage of stator winding shorted turns in three-phase induction motors. A MATLAB-based model was developed and simulated under different fault-load combination cases for different sizes of motors. The motor's developed electromechanical torque was selected as a fault indicator. For the design and training of the neural network, the mean, variance, max, min, and F120 time based on statistical and frequency-related features were found to be very distinct for correlating the captured electromechanical torque with its corresponding percentage of shorted turns. In the training phase of the neural network, five different motors were used and are referred to as seen motors. On the other hand, for testing the efficiency of the developed diagnostic tool, the electromechanical torque under different fault-load combination cases, previously never seen from the first five motors and those of two new motors (referred to as unseen), was used. Testing results revealed accuracy in the range of 88-99%.

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A novel indicator of stator winding inter-turn fault in induction motor using infrared thermal imaging

Cited by 48 publications

References 10 publications

Bir Asenkron Motorun Mekanik Titreşim Sinyallerinin Ölçülerek Arıza Analizinin Yapılması

Bir Asenkron Motorun Mekanik Titreşim Sinyallerinin Ölçülerek Arıza Analizinin Yapılması

An automated thermographic image segmentation method for induction motor fault diagnosis

An Efficient Stator Inter-Turn Fault Diagnosis Tool for Induction Motors

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