Induction motors (IMs) are widely used in industrial applications due to their robustness, low cost, and simple construction. However, the increasing integration of power electronic devices such as inverters, soft starters, and variable frequency drives (VFDs) has made modern energy systems more complex, introducing significant harmonic distortions. These harmonics alter the sinusoidal nature of supply voltages, causing additional losses, torque pulsations, overheating, and reduced efficiency. In particular, odd-order harmonics such as the 5th, 7th, 11th, and 13th adversely affect motor performance by generating reverse torques, increasing mechanical vibrations, and accelerating insulation and bearing degradation. To address this challenge, this study investigates harmonic estimation in IMs using a combination of real measurements and artificial intelligence. Voltage signals were acquired from motors under star-delta, soft starter, and VFD-fed conditions at an industrial facility. To augment the dataset and model real noise environments, conditional Generative Adversarial Networks (cGANs) were employed to generate synthetic signals at varying signal-to-noise ratios. A feedforward neural network was then trained with these real and synthetic signals to estimate the amplitudes of key harmonics. The proposed model, optimized using the Adam optimization algorithm significantly improved estimation accuracy, reducing the Mean Absolute Error (MAE) from 0.9008 to 0.2993 and the Root Mean Squared Error (RMSE) from 1.0195 to 0.4123. The proposed framework also achieved very accurate estimation of the 5th, 7th, 11th, and 13th harmonics compared to ground-truth measurements. These results demonstrate the potential of combining real-world measurements, synthetic data generation, and machine learning regression for accurate harmonic characterization in IMs, contributing to improved diagnostics, monitoring, and efficiency in industrial energy systems.
This paper proposes a knowledge-based input configuration to inform deep learning models for both electrical and mechanical fault diagnosis, rather than increasing model complexity, and confirms that, while conventional feature processing techniques perform well for electrical fault diagnosis, only the proposed FFT-informed input effectively captures both electrical and mechanical fault patterns.
Jingyi Yan, Hariram Arni, Bin Jou et al.· Measurement· 1 citation
Accurate fault detection in induction motors (IMs) under varying load conditions remains a critical challenge in industrial condition monitoring (CM). Inspired by the foundational work, which highlighted the impact of mechanical load on fault signature detectability. This study proposes a multi-modal signal analysis approach to bearing fault diagnosis using stator current, rotor speed, and flux-induced voltage signals. A custom fifteen-class dataset was collected, comprising healthy and faulty motor states at 0%, 50%, and 100% load levels. Unlike conventional approaches that rely on extensive preprocessing and handcrafted feature extraction, the proposed framework operates directly on raw signals, enabling a lightweight, computationally efficient, and easily deployable solution. This design significantly reduces implementation complexity while maintaining high diagnostic performance, making it suitable for real-time and industrial applications. Two types of models were evaluated in this study: traditional machine learning models and deep learning models. Experimental results demonstrate significant performance gains compared to single-sensor models, highlighting the benefits of cross-domain signal fusion. Models specifically designed to process time-series data, such as the Temporal Convolutional Network (TCN) and particularly the Long Short-Term Memory (LSTM), exhibit outstanding performance. During the training and validation phases, the LSTM model achieved perfect classification accuracy (100%), outperforming all other evaluated models. However, during the deployment-oriented evaluation on unseen test data, the SVM and TCN models demonstrated the most consistent generalization performance, achieving perfect prediction results across all tested samples. Recent architectures, such as the Transformer, also demonstrate strong potential; with careful hyperparameter tuning, their performance especially in terms of generalization can be further enhanced.
Kamal Hamani, M. Kuchař, Martin Sobek et al.· Scientific Reports· 0 citations
Induction Motors (IMs) are a very important group of electrical appliances used in industry, due to their strength, versatility, high reliability, and cost-effectiveness. However, due to various faults, there can be many issues, such as production downtime, energy losses, and increased maintenance costs. This paper employs Deep learning (DL) frameworks, such as Simple Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU), for the detection and categorization of IM. The novelty of the proposed work lies in the unified multi-sensor, multi-class, and multi-model DL framework with comprehensive cross-architecture evaluation for robust IM fault diagnosis under eight different operating scenarios, including startup transients and phase-removal faults, load-based faulty and healthy conditions. The results show that the DL is effective in electrical fault identification, using the classification accuracies of RNN—85.14%, LSTM −90.19%, BiLSTM—92.24%, and GRU—94.19%. The proposed approach can be used to differentiate between healthy and faulty operating conditions, which aim to highlight the effectiveness of DL for intelligent condition monitoring in induction motor drive-based machines.
R. Sooraj, S. Ramu, R. Sitharthan et al.· Scientific Reports· 0 citations
To maintain the safety and reliability of industrial applications, it is important to analyse faults in three-phase induction motors. Traditional methods have difficulty in identifying the complex characteristics of errors in three-phase induction motors because the signals associated with three-phase induction motors are often noisy and exhibit non-linear dynamics. In this paper, a comprehensive method to analyse errors in three-phase induction motors are proposed that utilises advanced signal preprocessing, feature extraction, and Deep Learning (DL) methods to address these issues. Initially, an Entropy-Driven Empirical Wavelet Filter (EDEWF) are applied to clean the raw motor signal, and a Min-Max scaling is used to normalise the signal to create useful features. Next, an integrated hybrid model combining a Radial Basis Function Neural Network (RBFNN) with a Multi-Head Adaptive Transformer (MHAT) are used for capturing local non-linear characteristics and global context. The features extracted from both models are combined into one feature vector and passed through a fully connected layer for classification. The Starfish Optimization Algorithm (SFOA) is implemented to adjust the hyperparameter of the proposed integrated hybrid model to improve the overall performance. The results obtained from the proposed method showed that it achieved a greatest accuracy, precision, recall, F1-Score of 0.9960 and robustness in the diagnostic of three-phase induction motors compared to traditional diagnostic methods, with respect to classifying the motor states as normal, healthy, and Broken Rotor Bar (BRB) faulted.
Killamsetti Vijeta, Perumal B Sri Suyambulinga, R. T. Kumar et al.· ITEGAM- Journal of Engineeri...· 0 citations
Unplanned failures of induction motors impose serious operational and financial penalties on industrial facilities, yet the fault signatures that precede such failures are detectable well in advance through careful sensor instrumentation and data-driven analysis. This paper presents an end-to-end Internet-of-Things (IoT) predictive maintenance scheme based on two off-the-shelf sensors: a DS18B20 one-wire digital thermometer and 2 piezo vibration sensor modules, with an ESP32 edge device for running the full machine learning pipeline offline, independent from any cloud services. Four operating scenarios are considered: healthy condition, BPFO (bearing outer race fault), misaligned shaft, and rotor imbalance. Based on 1-second sampling intervals, 17 descriptors are derived, including statistics in the time domain, Fourier harmonic peaks, energy ratios between different frequency bands, and temperature gradients measured across all sensors. A dual-stage feature selection method using mutual information (MI) score and Random Forest mean decrease impurity (MDI) ranking reduces the number of features to the 10 most relevant descriptors, reducing the computational complexity by 41% at the expense of 5.6% F1-macro. On a balanced 600-sample synthetic dataset, the resulting Random Forest classifier attains 87.3% hold-out accuracy, 91.0±1.9% five-fold cross-validation accuracy, and a macro area-under-the-ROC-curve of 0.980. End-to-end inference takes just 39 ms on the ESP32, easily meeting the 200 ms requirement for real-time alerting.
Akash Mastud, Dhiraj Vaidya, Azaroddin Sayyed et al.· International Conference on...· 0 citations
Motor Current Signature Analysis (MCSA) is a non-invasive technique that enables the detection of bearing faults in rotating electrical machines without the need for additional sensors. In this study, the Paderborn University bearing dataset was utilized to perform a two-stage analysis. In the first stage, motor current data were processed directly using 1-Dimensional Convolutional Neural Networks (1D-CNN). In the second stage, scalogram images obtained via Continuous Wavelet Transform (CWT) were used to train five different deep learning models, with the ResNet18-based 2D-CNN model providing the best performance. To prevent data leakage, the training and testing sets were partitioned based on individual bearings to ensure complete isolation. The experimental results demonstrated that both 1D-CNN and ResNet18-based 2D-CNN methods achieved 100% accuracy in detecting outer race faults. However, it was observed that the impact of inner race faults on the stator current remains weak due to the complex physical transmission path of the fault signal, resulting in significantly lower detection rates.
Y. Çekiç, Aydin Akan· Signal Processing and Commun...· 0 citations