Optimal parameters estimation of a BLDC motor by Kohonen's Self Organizing Map Method

Jaganathan, B.; Venkatesh, S.; Bhardwaj, Yougank; Sridhar, Vijay

doi:10.1109/raics.2011.6069274

Cited by 2 publications

(4 citation statements)

References 4 publications

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“…The WTM algorithm assumes that also neurons from an appropriately defined neighbourhood (e.g., Gauss neighbourhood function) can be activated (weight adaptation). Usually, Euclidean distance is taken as the neighbourhood measure [44][45][46].…”

Section: Kohonen Mapsmentioning

confidence: 99%

Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks

2021

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Permanent magnet synchronous motors (PMSMs) are becoming more popular, both in industrial applications and in electric and hybrid vehicle drives. Unfortunately, like the others, these are not reliable drives. As in the drive systems with induction motors, the rolling bearings can often fail. This paper focuses on the possibility of detecting this type of mechanical damage by analysing mechanical vibrations supported by shallow neural networks (NNs). For the extraction of diagnostic symptoms, the Fast Fourier Transform (FFT) and the Hilbert transform (HT) were used to obtain the envelope signal, which was subjected to the FFT analysis. Three types of neural networks were tested to automate the detection process: multilayer perceptron (MLP), neural network with radial base function (RBF), and Kohonen map (self-organizing map, SOM). The input signals of these networks were the amplitudes of harmonic components characteristic of damage to bearing elements, obtained as a result of FFT or HT analysis of the vibration acceleration signal. The effectiveness of the analysed NN structures was compared from the point of view of the influence of the network architecture and various parameters of the learning process on the detection effectiveness.

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Section: Kohonen Mapsmentioning

confidence: 99%

Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks

2021

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“…Since, a rule based fault detection is done, speed of detecting the fault is better in fuzzy based systems. Kohen's self organizing map technique has been used in (Jaganathan et al, 2011). This method uses i d and i q to estimate the performance.…”

Section: Introductionmentioning

confidence: 99%

“…This method uses i d and i q to estimate the performance. It is obvious that it takes about 500 epochs as given in (Jaganathan et al, 2011). The data base involved in the estimation process is very huge and time consuming.…”

Section: Introductionmentioning

confidence: 99%

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On the Detection of Open and Short Fault in Switched Reluctance Motor With Classification

Chandrika¹,

Jeyakumar²

2014

Journal of Computer Science

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Switched reluctance motors are tremendously increasing in usage. Monitoring the motor performance online is essential, in order to maintain the health of the motor as well as for a continual application process carried out by the SRM. Though certain fault detection methods are available based on comparator circuits, those methods are inefficient, because they need human attention to identify the faults. In this study, we propose a wavelet based SRM power drive fault detection and classification using BPN method. The currents flowing in different phases and the known states of all power switches helps us to find out the magnitude of the converter supply current, since an asymmetric bridge converter is used. In this study, short and open circuit fault occurrences in the converter power switches are presented for various coils of the stator windings. The proposed technique is able to detect these faults and identify the affected stator phase as well as the faulty arm along with the the time instant of fault occurrence.

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Optimal parameters estimation of a BLDC motor by Kohonen's Self Organizing Map Method

Cited by 2 publications

References 4 publications

Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks

Effectiveness Analysis of PMSM Motor Rolling Bearing Fault Detectors Based on Vibration Analysis and Shallow Neural Networks

On the Detection of Open and Short Fault in Switched Reluctance Motor With Classification

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