Vectorized Mathematical Model of a Slip-Ring Induction Motor

Wciślik, M.; Suchenia, K.; Cyganik, Andrzej

doi:10.3390/en13154015

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Technical indicators are gradually approaching the direction of zero wear and zero maintenance [ 2 ]. Therefore, scholars have conducted in-depth research on the contact form [ 3 , 4 , 5 ], material wear [ 6 ], and performance reliability of conductive slip rings [ 7 ]. The position of the brush wire in contact with the slip ring directly affects the amount of pressure in contact with the slip ring.…”

Section: Introductionmentioning

confidence: 99%

Machine Vision-Based Method for Measuring and Controlling the Angle of Conductive Slip Ring Brushes

Liu

Wang

et al. 2022

Micromachines

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The conductive slip ring is used for power or signal transmission between two objects rotating relative to each other. It has become an essential part of modern industrial development. In traditional automated production measurements, the typical method is to use calipers, goniometers, or angle gauges to measure a parameter of the workpiece several times and then average it. These inspection means have low measurement accuracy and slow measurement speed, and measurement data cannot be processed in a timely manner. A machine vision-based method for measuring and controlling the angle of the brushes is proposed for this problem. First, the brush angle forming device was built for the conductive slip ring brush wire, forming the principle and rebound characteristics. Then, machine vision and image processing algorithms were applied to measure the key parts of the conductive slip ring brushes. The data of the forming angle value and rebound angle value were obtained during the forming process of the brush wire angle. Finally, a pre-compensation model for the brush filament rebound was developed and validated based on the curve fitting method. The test results show that the error of the angle measurement is within 0.05°. The average error of the measured rebound angle and the calculated rebound angle of the brush filament pre-compensation model was 0.112°, which verifies the correctness of the pre-compensation model. The forming angle can be controlled more precisely, and the contact performance between the brush wire and the ring body can be improved effectively. This method has the potential to be extended to engineering applications.

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Section: Introductionmentioning

confidence: 99%

Machine Vision-Based Method for Measuring and Controlling the Angle of Conductive Slip Ring Brushes

Liu

Wang

et al. 2022

Micromachines

View full text Add to dashboard Cite

show abstract

Enhancing machine learning multi-class fault detection in electric motors through entropy-based analysis

Palaiologou,

Falekas,

Antonino-Daviu

et al. 2024

Meas. Sci. Technol.

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In the field of electric motor maintenance, this study introduces a transformative approach by inte-grating entropy-based algorithms with machine learning for enhanced multi-class fault detection. Employing Shannon, Renyi, and Tsallis entropy algorithms on standard fault detection measure-ments, the research significantly advances predictive maintenance strategies through a robust, ear-ly-indication, system-agnostic analysis. Detailed examination is conducted, comparing results de-rived from datasets that include statistical features (excluding entropy) against the proposed entro-py-based datasets, when applied to a Multi-Layer Perceptron Classifier. Optimization of the MLPC and all compared algorithms’ hyperparameters is done using the state-of-the-art Optuna tool to dynamically explore each search space, ensuring that each methodology performs adequately in a timely fashion while allowing for adaptation. The results showcase significant enhancement in clas-sification accuracy of diverse electric motor operational states, facilitating the differentiation be-tween healthy and various levels of fault conditions under assorted load scenarios. Computational analyses reveal favorable results related to execution time and memory overhead, thereby support-ing the practicality in operations constrained by memory resources. Validation of the approach is achieved through laboratory experiments on a purpose-built test bench. Versatility of entropy-based measures through their proposed utilization in diverse fault indications is achieved by a demonstration in the field of mechanical fault detection with a focus on bearing faults through well-respected datasets.

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Vectorized Mathematical Model of a Slip-Ring Induction Motor

Cited by 2 publications

References 9 publications

Machine Vision-Based Method for Measuring and Controlling the Angle of Conductive Slip Ring Brushes

Machine Vision-Based Method for Measuring and Controlling the Angle of Conductive Slip Ring Brushes

Enhancing machine learning multi-class fault detection in electric motors through entropy-based analysis

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