This paper introduces a mesh-based magnetic equivalent circuit (MEC) modeling technique for induction machines (IMs) in healthy and broken rotor bars conditions. The MEC model is presented as a highly accurate and computationally efficient alternative to finite element (FE) models. By incorporating modifications to the air gap coupling method, including a new Lagrange interpolation function, and utilizing a harmonic MEC model, the accuracy of the solution is improved while reducing electrical and mechanical transients. Compared to experiments and 2D FE models, this model achieves precise results for electromagnetic torque, rotational speed, and forces across various conditions. The Lagrange interpolation function forms the basis for the air gap coupling between stator and rotor flux densities. The results demonstrate the MEC model’s exceptional accuracy in predicting speed oscillations, calculating forces, and analyzing current harmonics in faulty IMs. Furthermore, the MEC model performs over 30 times faster than the 2D FE models.

This paper proposes a new surrogate optimization routine for optimal design of a direct on line (DOL) squirrel cage induction motor. The geometry of the motor is optimized to maximize its electromagnetic efficiency while respecting the constraints, such as output power and power factor. The routine uses the methodologies of Latin-hypercube sampling, a clustering technique and a Box–Behnken design for improving the accuracy of the surrogate model while efficiently utilizing the computational resources. The global search-based particle swarm optimization (PSO) algorithm is used for optimizing the surrogate model and the pattern search algorithm is used for fine-tuning the surrogate optimal solution. The proposed surrogate optimization routine achieved an optimal design with an electromagnetic efficiency of $93.90\%$, for a 7.5 kW motor. To benchmark the performance of the surrogate optimization routine, a comparative analysis was carried out with a direct optimization routine that uses a finite element method (FEM)-based machine model as a cost function.

In this article, a forward uncertainty propagation method is presented for a 2-D finite-element (FE) model in an induction machine. This method is applied to quantify the uncertainty of input parameters, for example, dimensions and material properties, and demonstrate their variability effect on harmonics related to the broken rotor bar (BRB) faults. To show the most influential input parameters in the case of BRB harmonics, a global sensitivity analysis is performed from the polynomial chaos expansion (PCE) approximation of the FE model. The results of this study indicate that BRB harmonics are highly sensitive to stator inner diameter, rotor outer diameter, rotor bar conductivity, and core materials. Moreover, the combined variability of these sensitive input parameters can attenuate the amplitude of the BRB harmonics 30%–90% compared to the simulation results at nominal values of input parameters and closely match with measurement results.

This article presents a novel data augmentation method that generates feature values for unmeasured loading levels based on limited measured and simulated loading level data. The incorporation of offline simulated data in the augmentation framework and the mapping of the error distribution over the loading levels greatly reduce the dependency on including a large number of loading levels in the curve fitting process. Furthermore, the proposed method shows high potential to minimize the deviation between measured and simulated data at the feature level. The method is applied to the induction machine to generate feature values at 25% and 50% loading levels for healthy, one, two, and three broken rotor bars conditions. An excellent agreement is observed between the augmented and actual feature values calculated from the measured data at 25% and 50% loading levels. The inclusion of this augmented data in the training phase aids in resolving the generalization issue and enhancing the average classification accuracy of the XGBoost algorithm by 9.4% and 4.4% at 25% and 50% loading levels, respectively.

This study addresses the challenges of machine learning‐based condition monitoring of induction machines under varying load conditions, which can result in low accuracy at unmeasured loading levels. A hybrid data augmentation framework is developed that combines multiple regression models and ensemble learning techniques to generate feature values at any unmeasured loading levels. The proposed method requires feature computation from only four measured loading levels under healthy, one, two and three broken rotor bars conditions as training data, enabling feature values augmentation for other loading levels. In this study, the augmentation method is applied to generate feature values at two intermediate levels (50% and 75%) and one extreme level (100%) and the corresponding results are presented. This hybrid data augmentation method not only produces accurate feature values for intermediate loading levels but also performs exceptionally well in extrapolating feature values at extreme loading levels. Incorporating this generated data during the training phase resolves generalisation issues and substantially improves the classification accuracy of machine learning models. In particular, the integration of ensemble learning techniques helped to increase accuracy from 38.75%, 42.75% and 60%–100% for the K‐nearest neighbours, support vector machine and decision tree models, respectively, at the 100% loading level.