No abstract

Rotor bars are one of the most failure-critical components in induction machines. We present an approach for developing a rotor bar fault identification classifier for induction machines. The developed machine learning-based models are based on simulated electrical current and vibration velocity data and measured vibration acceleration data. We introduce an approach that combines sequential model-based optimization and the nested cross-validation procedure to provide a reliable estimation of the classifiers’ generalization performance. These methods have not been combined earlier in this context. Automation of selected parts of the modeling procedure is studied with the measured data. We compare the performance of logistic regression and CatBoost models using the fast Fourier-transformed signals or their extracted statistical features as the input data. We develop a technique to use domain knowledge to extract features from specific frequency ranges of the fast Fourier-transformed signals. While both approaches resulted in similar accuracy with simulated current and measured vibration acceleration data, the feature-based models were faster to develop and run. With measured vibration acceleration data, better accuracy was obtained with the raw fast Fourier-transformed signals. The results demonstrate that an accurate and fast broken rotor bar detection model can be developed with the presented approach.

New methods to perform time series classification arise frequently and multiple state-of-theart approaches achieve high performance on benchmark datasets with respect to accuracy and computation time. However, often the modeling procedures do not include proper validation but rather rely only on either external test dataset or one-level cross-validation. ATSC-NEX is an automated procedure that employs sequential model-based optimization together with nested cross-validation to build an accurate and properly validated time series classification model. It aims to find an optimal pipeline configuration that includes the selection of input type and settings, as well as model type and hyperparameters. The results of a case study in which a model for the identification of diesel engine type is developed, show that the algorithm can efficiently find a well-performing pipeline configuration. The comparison between ATSC-NEX and some state-of-the-art methods on several benchmark datasets shows that similar accuracy can be achieved.

A transient model of an induction machine is developed in this work using an artificial neural network surrogate model. The model is suitable to be used for direct-on-line induction machines. Finite element based model of induction machine is used to generate the training, validation, and testing datasets. Different inputs and model configurations are investigated to find an optimal solution in developing the transient model. The proposed transient model is suitable to be used in digital twin services, since it can estimate the current and torque accurately in real time based on only voltage and measured shaft speed.