A Review of Thermal Monitoring Techniques for Radial Permanent Magnet Machines

Meng, Tianze; Zhang, Pinjia

doi:10.3390/machines10010018

Cited by 18 publications

(9 citation statements)

References 112 publications

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“…[18,19] present models and iterative algorithms with measurable quantities to estimate temperature values for a permanent magnet synchronous motor. However, from a technical point of view, these methods can be categorized, as shown in Figures 1 and 2 [20]. Over the last decade, artificial intelligence techniques such as particle swarm optimization (PSO), neural networks (NNs), and the genetic algorithm have been utilized in temperature monitoring [20].…”

Section: Prediction Of Stator Winding Temperaturementioning

confidence: 99%

“…However, from a technical point of view, these methods can be categorized, as shown in Figures 1 and 2 [20]. Over the last decade, artificial intelligence techniques such as particle swarm optimization (PSO), neural networks (NNs), and the genetic algorithm have been utilized in temperature monitoring [20]. This paper uses historical data on stator winding temperatures to predict temperature behavior over time and, hence, provide information for preventive maintenance.…”

Section: Prediction Of Stator Winding Temperaturementioning

confidence: 99%

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Monitoring of Stator Winding Insulation Degradation through Estimation of Stator Winding Temperature and Leakage Current

Szamel,

Oloo

2024

Machines

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Switched Reluctance Motors (SRMs), Permanent Magnet Synchronous Motors (PMSMs), and induction motors may experience failures due to insulation-related breakdowns. The SRM rotor is of a non-salient nature and made of solid steel material. There are no windings on the rotor. However, the stator is composed of windings that are intricately insulated from each other using materials such as enamel wire, polymer films, mica tapes, epoxy resin, varnishes, or insulating tapes. The dielectric strength of the insulation may fail over time due to several environmental factors and processes. Dielectric breakdown of the winding insulation can be caused by rapid switching of the winding current, the presence of contaminants, and thermal aging. For reliable and efficient operation of the SRMs and other electrical machines, it is necessary to take into account the physics of the winding insulation and perform appropriate diagnostics and estimations that can monitor the integrity of the insulation. This article presents the estimation problem using a Genetic Algorithm (GA)-optimized Random Forest Regressor. Empirical properties and measurable quantities in the historical data are utilized to derive temperature and leakage current estimation. The developed model is then combined with a moving average function to increase the accuracy of prediction of the stator winding temperature and leakage current. The performance of the model is compared with that of the Feedforward Neural Network and Long Short-Term Memory over the same winding temperature and leakage current historical data. The performance metrics are based on computation of the Mean Square Error and Mean Absolute Error.

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Section: Prediction Of Stator Winding Temperaturementioning

confidence: 99%

Section: Prediction Of Stator Winding Temperaturementioning

confidence: 99%

Monitoring of Stator Winding Insulation Degradation through Estimation of Stator Winding Temperature and Leakage Current

Szamel,

Oloo

2024

Machines

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“…Deviations from the normal state can be detected by monitoring the actual state of the EM, and, with this, critical damage to the machine caused by faults can be prevented. Vaimann et al [2] and Meng et al [3] indicated that the monitoring of the temperature inside a radial permanent magnet machine is a significant aspect in preventing failure. Regarding the magnets of such an EM, measuring the magnets' temperature prevents demagnetization and hence a loss of performance up to total failure.…”

Section: Introductionmentioning

confidence: 99%

Approach to Design of Piezoelectric Energy Harvester for Sensors on Electric Machine Rotors

Brandl,

Reuss,

Heidle

2024

Energies

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The reliability and efficiency of components are key aspects in the automotive industry. Electric machines become the focus of development. Thus, improvements in efficiency and reliability have gained significance. While it is established to attach sensors to the fixed parts of machines, such as stators, moving parts like rotors pose a major challenge due to the power supply. Piezoelectric generators can operate as energy harvesters on rotors and thus enable the rotor-based integration of sensors. The research in this article proposes the first approach to the design of a piezoelectric energy harvester (PEH) for an electric machine rotor dedicated to powering a wireless sensor system. After introducing the field of PEHs, the integration of the proposed device on a rotor shaft is presented. Further, a gap between the provided and needed data for the design of a PEH is identified. To overcome this gap, a method is presented, starting with the definition of the rotor shaft dimensions and the applied mechanical loads, including a method for the calculation of the imbalance of the rotor. With the first set of dimensions of the shaft and PEH, a co-simulation is performed to calculate the power output of this rotor and PEH set. The results of the simulation indicate the feasible implementation of the PEH on the rotor, providing enough energy to power a temperature sensor.

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“…On the contrary, the use of thermal dynamics [8], [9] or artificial intelligence (AI) technology [10] to indirectly measure the temperature of PMSMs represents a more efficient and cost-effective approach. Therefore, the modeling and analysis of the thermal dynamics of the PMSM have received extensive attention [11]- [14].…”

Section: Introductionmentioning

confidence: 99%

Explainable Neural Dynamics Models for Motor Temperature Prediction

Liao,

Chen,

Long

et al. 2023

Preprint

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<p>The permanent magnet synchronous motor finds extensive use in industrial applications, and the development of effective thermal management solutions is crucial to enhance its power density. Accurate temperature prediction of the permanent magnet synchronous motor serves as the fundamental basis for designing effective thermal management strategies. Model-based prediction methods exhibit superior real-time performance, but the intricate modeling process requires substantial expert knowledge guidance and lacks versatility. Conversely, data-driven prediction methods, while offering flexibility, often lack physical implications in terms of system dynamics. This paper proposed a structured linear neural dynamics model for motor temperature prediction. This model is data-driven, with prior knowledge integrated into its structure, which preserves flexibility while guaranteeing system stability through the Perron-Frobenius theorem. Additionally, this paper achieves the decoupling of control input from state transitions and the embedded deployment of this model. The method is validated with a real dataset. The lightweight feature is demonstrated by the implementation of an STM32 Microcontroller with 1.808 KB and 27 mW. The paper is accompanied with the open source data and code at GitHub: https://github.com/ms140429/Explainable-Neural-Dynamics-Model</p>

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A Review of Thermal Monitoring Techniques for Radial Permanent Magnet Machines

Cited by 18 publications

References 112 publications

Monitoring of Stator Winding Insulation Degradation through Estimation of Stator Winding Temperature and Leakage Current

Monitoring of Stator Winding Insulation Degradation through Estimation of Stator Winding Temperature and Leakage Current

Approach to Design of Piezoelectric Energy Harvester for Sensors on Electric Machine Rotors

Explainable Neural Dynamics Models for Motor Temperature Prediction

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