KS-Net: Multi-layer network model for determining the rotor type from motor parameters in interior PMSMs

• URL: http://arxiv.org/abs/2510.15688v1
• Date: Fri, 17 Oct 2025 14:32:05 GMT
• Title: KS-Net: Multi-layer network model for determining the rotor type from motor parameters in interior PMSMs
• Authors: Kivanc Dogan, Ahmet Orhan,
• Abstract summary: This study shows that the rotor shape of IPMSMs can be predicted with high accuracy using data-driven approaches.<n>The findings provide a solid foundation for accelerating motor design processes, developing automated rotor identification systems, and enabling data-driven fault diagnosis in engineering applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The demand for high efficiency and precise control in electric drive systems has led to the widespread adoption of Interior Permanent Magnet Synchronous Motors (IPMSMs). The performance of these motors is significantly influenced by rotor geometry. Traditionally, rotor shape analysis has been conducted using the finite element method (FEM), which involves high computational costs. This study aims to classify the rotor shape (2D type, V type, Nabla type) of IPMSMs using electromagnetic parameters through machine learning-based methods and to demonstrate the applicability of this approach as an alternative to classical methods. In this context, a custom deep learning model, KS-Net, developed by the user, was comparatively evaluated against Cubic SVM, Quadratic SVM, Fine KNN, Cosine KNN, and Fine Tree algorithms. The balanced dataset, consisting of 9,000 samples, was tested using 10-fold cross-validation, and performance metrics such as accuracy, precision, recall, and F1-score were employed. The results indicate that the Cubic SVM and Quadratic SVM algorithms classified all samples flawlessly, achieving 100% accuracy, while the KS-Net model achieved 99.98% accuracy with only two misclassifications, demonstrating competitiveness with classical methods. This study shows that the rotor shape of IPMSMs can be predicted with high accuracy using data-driven approaches, offering a fast and cost-effective alternative to FEM-based analyses. The findings provide a solid foundation for accelerating motor design processes, developing automated rotor identification systems, and enabling data-driven fault diagnosis in engineering applications.

Related papers

• Gradient Networks for Universal Magnetic Modeling of Synchronous Machines [39.146761527401424]
  This paper presents a physics-informed neural network approach for dynamic modeling of saturable synchronous machines.<n>We introduce an architecture that incorporates gradient networks directly into the fundamental machine equations.<n>We validate the proposed approach using measured and finite-element method (FEM) datasets from a 5.6-kW permanent-magnet synchronous machine.
  arXiv  Detail & Related papers  (2026-02-16T17:28:42Z)
• Learning to Hear Broken Motors: Signature-Guided Data Augmentation for Induction-Motor Diagnostics [40.044978986425676]
  The application of machine learning (ML) algorithms in the intelligent diagnosis of three-phase engines has the potential to significantly enhance diagnostic performance and accuracy.<n>We propose a novel unsupervised anomaly generation methodology that takes into account the engine physics model.<n>We propose Signature-Guided Data Augmentation (SGDA), an unsupervised framework that synthesizes physically plausible faults directly in the frequency domain of healthy current signals.
  arXiv  Detail & Related papers  (2025-06-10T03:36:16Z)
• AutoML for Multi-Class Anomaly Compensation of Sensor Drift [44.63945828405864]
  Sensor drift degrades the performance of machine learning models over time.<n>Standard cross-validation method overestimates performance by inadequately accounting for drift.<n>This paper presents two solutions: (1) a novel sensor drift compensation learning paradigm for validating models, and (2) automated machine learning (AutoML) techniques to enhance classification performance and compensate sensor drift.
  arXiv  Detail & Related papers  (2025-02-26T14:34:53Z)
• Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing [53.77822620185878]
  We propose a synergistic methodology to concurrently optimize perovskite memristor fabrication and develop robust analog DNNs.<n>We develop "BayesMulti", a training strategy utilizing BO-guided noise injection to improve the resistance of analog DNNs to memristor imperfections.<n>Our integrated approach enables use of analog computing in much deeper and wider networks, achieving up to 100-fold improvements.
  arXiv  Detail & Related papers  (2024-12-03T19:20:08Z)
• Predictive Maintenance Model Based on Anomaly Detection in Induction Motors: A Machine Learning Approach Using Real-Time IoT Data [0.0]
  In this work, we demonstrate a novel anomaly detection system on induction motors used in pumps, compressors, fans, and other industrial machines. We use a combination of pre-processing techniques and machine learning (ML) models with a low computational cost.
  arXiv  Detail & Related papers  (2023-10-15T18:43:45Z)
• Introducing a Deep Neural Network-based Model Predictive Control Framework for Rapid Controller Implementation [41.38091115195305]
  This work presents the experimental implementation of a deep neural network (DNN) based nonlinear MPC for Homogeneous Charge Compression Ignition (HCCI) combustion control. Using the acados software package to enable the real-time implementation of the MPC on an ARM Cortex A72, the optimization calculations are completed within 1.4 ms. The IMEP trajectory following of the developed controller was excellent, with a root-mean-square error of 0.133 bar, in addition to observing process constraints.
  arXiv  Detail & Related papers  (2023-10-12T15:03:50Z)
• End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control [45.84205238554709]
  We present a method for reinforcement learning of Koopman surrogate models for optimal performance as part of (e)NMPC. We show that the end-to-end trained models outperform those trained using system identification in (e)NMPC.
  arXiv  Detail & Related papers  (2023-08-03T10:21:53Z)
• Multi-Objective Optimization of Electrical Machines using a Hybrid Data-and Physics-Driven Approach [0.0]
  We present the application of a hybrid data-and physics-driven model for numerical optimization of permanent magnet synchronous machines (PMSM) Following the data-driven supervised training, deep neural network (DNN) will act as a meta-model to characterize the electromagnetic behavior of PMSM. These intermediate measures are then post-processed with various physical models to compute the required key performance indicators.
  arXiv  Detail & Related papers  (2023-06-15T12:47:56Z)
• Support Vector Machine for Determining Euler Angles in an Inertial Navigation System [55.41644538483948]
  The paper discusses the improvement of the accuracy of an inertial navigation system created on the basis of MEMS sensors using machine learning (ML) methods. The proposed algorithm based on MO has demonstrated its ability to correctly classify in the presence of noise typical for MEMS sensors.
  arXiv  Detail & Related papers  (2022-12-07T10:01:11Z)
• Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization [0.0]
  The present work describes and validates an automated active learning approach, AutoML-GA, for surrogate-based optimization of internal combustion engines. A genetic algorithm is employed to locate the design optimum on the machine learning surrogate surface. It is demonstrated that AutoML-GA leads to a better optimum with a lower number of CFD simulations.
  arXiv  Detail & Related papers  (2021-01-07T17:50:52Z)
• Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems [1.0773924713784704]
  This paper proposes novel machine learning models for intelligent road inspection. The proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the pavement condition index ( PCI) The results of the analysis have been verified through using four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE) and standard error (SD)
  arXiv  Detail & Related papers  (2020-01-18T19:12:51Z)
• Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning [0.0]
  Monitoring the magnet temperature in permanent magnet synchronous motors (PMSMs) for automotive applications is a challenging task. Overheating results in severe motor deterioration and is thus of high concern for the machine's control strategy and its design. Several machine learning (ML) models are empirically evaluated on their estimation accuracy for the task of predicting latent high-dynamic magnet temperature profiles.
  arXiv  Detail & Related papers  (2020-01-17T11:41:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.