Investigation of long short-term memory networks to temperature prediction for permanent magnet synchronous motors

Wallscheid, Oliver; Kirchgässner, Wilhelm; Böcker, Joachim

doi:10.1109/ijcnn.2017.7966088

Cited by 47 publications

(17 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although RNNs have been studied on the temperature estimation task in PMSMs before in [18], they are co-evaluated in this work's hyperparameter search in order to ensure comparability to the following newer topology.…”

Section: Neural Network Architectures a Recurrent Architectures mentioning

confidence: 99%

Estimating Electric Motor Temperatures With Deep Residual Machine Learning

Kirchgässner

Wallscheid

Böcker

2021

IEEE Trans. Power Electron.

Self Cite

110

View full text Add to dashboard Cite

Most traction drive applications lack accurate temperature monitoring capabilities, ensuring safe operation through expensive oversized motor designs. Classic thermal modeling requires expertise in model parameter choice, which is affected by motor geometry, cooling dynamics, and hot spot definition. Moreover, their major advantage over data-driven approaches, which is physical interpretability, tends to deteriorate as soon as their degrees of freedom are curtailed in order to meet the real-time requirement. In this work, deep recurrent and convolutional neural networks (NNs) with residual connections are empirically evaluated for their feasibility on predicting latent high-dynamic temperatures continuously inside permanent magnet synchronous motors (PMSMs). Here, the temperature profile in the stator teeth, winding, and yoke as well as the rotor's permanent magnets are estimated while their ground truth is available as test bench data. With an automated hyperparameter search through Bayesian optimization and a manual merge of target estimators into a multi-head architecture, lean models are presented that exhibit strong estimation performance at minimal model sizes. It has been found that the mean squared error and maximum absolute deviation performances of both, deep recurrent and convolutional NNs with residual connections, meet those of classic thermodynamics-based approaches, without requiring domain expertise nor specific drive train specifications for their topological design. Finally, learning curves for varying training set sizes and interpretations of model estimates through expected gradients are presented.

show abstract

Section: Neural Network Architectures a Recurrent Architectures mentioning

confidence: 99%

Estimating Electric Motor Temperatures With Deep Residual Machine Learning

Kirchgässner

Wallscheid

Böcker

2021

IEEE Trans. Power Electron.

Self Cite

110

View full text Add to dashboard Cite

show abstract

“…Typical examples are recurrent networks with internal memory cells or convolutional neural networks with large time spans of past data at their input (cf. [106], [115]). Recently, state-space neural networks with a special recurrent structure mimicking state-space model dynamics have been also introduced [118].…”

Section: Available Publications and Interim Conclusionmentioning

confidence: 99%

Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges

Wallscheid

2021

IEEE Open J. Ind. Applicat.

Self Cite

View full text Add to dashboard Cite

Temperature sensing in electric motors is an important task to ensure component protection against excessive heat while maximizing the power and torque capabilities. In order to optimize the dynamic performance limits of a motor during online operation, important motor temperatures must be known in real time. Since temperature measurements are associated with costs and integration efforts, model-based estimation methods became highly relevant. In recent years, many promising contributions have been made to this field leading to a vast literature basis. This paper organizes the literary landscape and gives a bird's-eye overview of the three most important estimation classes: Indirect methods, which track temperature-sensitive electrical motor parameters, and direct methods, namely lumped-parameter thermal networks as well as supervised machine learning. Practical limitations as well as special challenges of the respective methods are emphasized throughout the text. The aim of the paper is therefore to facilitate the entry for interested researchers and engineers in this field as well as to stimulate new research impulses. Hence, open problems as well as future research questions are addressed at the end of this review.

show abstract

“…This means that predictive modeling does not require knowledge of the material properties of a given device or having expertise knowledge about its construction. Both neural networks and other machine learning methods have proven their effectiveness in estimating the temperature of induction motors [ 28 ], permanent magnets synchronous motors [ 9 , 29 , 30 , 31 ], as well as brushed DC motors [ 32 , 33 ]. Many of the articles on PMSM temperature prediction using machine learning available in the literature use the motor coolant temperature as an input variable of the algorithm [ 9 , 29 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Sensorless Temperature Estimation of a BLDC Motor

Czerwiński

Gęca

Kolano

2021

Sensors

View full text Add to dashboard Cite

In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h of motor operation, and then, they were preprocessed. The algorithms of linear regression, ElasticNet, stochastic gradient descent regressor, support vector machines, decision trees, and AdaBoost were used for predictive modeling. The ability of the models to generalize was achieved by hyperparameter tuning with the use of cross-validation. The conducted research led to promising results of the winding temperature estimation accuracy. In the case of sensorless temperature prediction (model 1), the mean absolute percentage error MAPE was below 4.5% and the coefficient of determination R2 was above 0.909. In addition, the extension of the model with the temperature measurement on the casing (model 2) allowed reducing the error value to about 1% and increasing R2 to 0.990. The results obtained for the first proposed model show that the overheating protection of the motor can be ensured without direct temperature measurement. In addition, the introduction of a simple casing temperature measurement system allows for an estimation with accuracy suitable for compensating the motor output torque changes related to temperature.

show abstract

Investigation of long short-term memory networks to temperature prediction for permanent magnet synchronous motors

Cited by 47 publications

References 14 publications

Estimating Electric Motor Temperatures With Deep Residual Machine Learning

Estimating Electric Motor Temperatures With Deep Residual Machine Learning

Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges

Machine Learning for Sensorless Temperature Estimation of a BLDC Motor

Contact Info

Product

Resources

About