Kohonen's Self Organizing Map method of estimation of optimal parameters of a Permanent Magnet Synchronous Motor drive

Jaganathan, B.; Venkatesh, S.; Bhardwaj, Yougank; Prakash, C. Arun

doi:10.1109/iicpe.2011.5728132

Cited by 4 publications

(2 citation statements)

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“…Permanent magnets synchronous motors (PMSMs) are currently replacing induction motors (IM) in a broad range of industrial applications due to their many advantages. They are distinguished by high efficiency, high speed, high torque-to-inertia and current ratio (Jaganathan et al, 2011). But a very good parameter of motor does not mean a failure cannot happen.…”

Section: Introductionmentioning

confidence: 99%

“…Self-organising maps (SOMs), also known as Kohonen neural network (KNN), are less frequently used as damage classifiers of mechanical faults. In the literature, we can find examples of using SOM for estimation of optimal parameters of motor (Jaganathan et al, 2011), damage classifying of electrical faults (Skowron et al, 2019), or detection and classification of rolling bearing damage (Ewert et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Effectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive

Jankowska

Ewert

2021

Power Electronics and Drives

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Due to their many advantages, permanent magnet synchronous motors (PMSMs) are increasingly used in not only industrial drive systems but also electric and hybrid vehicle drives, aviation and other applications. Unfortunately, PMSMs are not free from damage that occurs during their operation. It is assumed that about 40% of the damage that occurs is related to rolling bearing damage. This article focuses on the use of Kohonen neural network (KNN) for rolling bearing damage detection in a PMSM drive system. The symptoms from the fast Fourier transform (FFT) and Envelope (ENV) Analysis of the mechanical vibration acceleration signal were analysed. The signal ENV was obtained by applying the Hilbert transform (HT). Two neural network functions are discussed: a detector and a classifier. The detector detected the damage and the classifier determined the type of damage to the rolling bearing (undamaged bearing, damaged rolling element, outer or inner race). The effectiveness of the analysed networks from the point of view of the applied signal processing method, map size, type of neighbourhood radius, distance function and the influence of input data normalisation are presented. The results are presented in the form of a confusion matrix, together with 2D and 3D maps of active neurons.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive

Jankowska

Ewert

2021

Power Electronics and Drives

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Surface reconstruction techniques: a review

Lim

Haron

2012

Artif Intell Rev

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A secure fast 2D — Discrete fractional fourier transform based medical image compression using hybrid encoding technique

Kumari¹,

Thanushkodi

2013

2013 International Conference on Current Trends in Engineering and Technology (ICCTET)

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Kohonen's Self Organizing Map method of estimation of optimal parameters of a Permanent Magnet Synchronous Motor drive

Cited by 4 publications

References 5 publications

Effectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive

Effectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive

Surface reconstruction techniques: a review

A secure fast 2D — Discrete fractional fourier transform based medical image compression using hybrid encoding technique

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Kohonen's Self Organizing Map method of estimation of optimal parameters of a Permanent Magnet Synchronous Motor drive

Cited by 4 publications

References 5 publications

Effectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive

Effectiveness Analysis of Rolling Bearing Fault Detectors Based On Self-Organising Kohonen Neural Network – A Case Study of PMSM Drive

Surface reconstruction techniques: a review

A secure fast 2D &#x2014; Discrete fractional fourier transform based medical image compression using hybrid encoding technique

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Product

Resources

About

A secure fast 2D — Discrete fractional fourier transform based medical image compression using hybrid encoding technique