Browsing by Author "Rajabioun, Ramin"

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Classification of Distributed Bearing Faults Using a Novel Sensory Board and Deep Learning Networks With Hybrid Inputs
(Ieee-inst Electrical Electronics Engineers inc, 2024) Rajabioun, Ramin; Afshar, Mojtaba; Atan, Ozkan; Mete, Mutlu; Akin, Bilal
Distributed bearing faults are the most common ones in industry and create random vibration patterns, which make their detection difficult. They are caused by lubrication issues, contamination issues, electrical erosion, bearing roughness, or the spread of a local fault. This research mainly focuses on the distrusted bearing faults diagnosis using a multi-sensory kit. For this purpose, a novel deep-learning framework is proposed to detect these faults using 3 axis vibrations and one stray magnetic flux signal. The data is collected at 50 operating points, i.e., 10 speed and 5 torque levels. The proposed architecture benefits from a multi-input pipeline consisting of time-frame signals and extracted features. A feature-rich architecture is proposed combining convolutional and high-level information. Although a deep learning structure coherently learns from the features through convolutional and LSTM layers, 20 predefined features sampled from each instance are also fed into the network to improve accuracy. The robustness of the overall system is validated with train/test split data. Deep learning results are compared with two more classification algorithms, SVM and XGBoost. The high accuracy of the proposed model demonstrates the superiority of the deep learning architecture for distributed bearing fault detection.
Current-Driven Deep Learning for Enhanced Motor Bearing Prognostics
(Ieee-inst Electrical Electronics Engineers inc, 2025) Afshar, Mojtaba; Rajabioun, Ramin; Akin, Bilal
An innovative deep-learning framework embedding residual and channel attention blocks is introduced, tailored explicitly to assess the remaining lifespan of cooling motor bearings affected by distributed faults, utilizing only 3-phase current signals. Cooling fan motors are integral to the stability of power electronics systems and data centers. Using an accelerometer to monitor bearings is not very practical for small motors and is costly. Hence different from other studies employing vibration signals for high-power machines, this research prioritizes motor current signals. Aging-related defects are clearly visible on small motor currents, unlike the large ones since the rated torque is low and the modulated torque disturbance caused by bearing defects is more noticeable on the phase currents. The experimental approach involves subjecting motors to diverse testing conditions, spanning from normal operation to failure, to ascertain their RUL before potential critical issues emerge. Seven configurations, encompassing permanent magnet synchronous motors and brushless direct current motors with varying power ratings, undergo rigorous experimental testing from normal to failure states. The resultant diverse dataset forms the basis for developing a robust distributed bearing fault detection algorithm. The proposed deep learning architecture demonstrates notable performance with a train accuracy of 97.10% and a test accuracy of 95.94%. This suggests a high level of effectiveness and generalization capability in accurately predicting the RUL in cooling fan motors using current signals.
Optimal Source Selection for Distributed Bearing Fault Classification Using Wavelet Transform and Machine Learning Algorithms
(MDPI, 2025) Rajabioun, Ramin; Atan, Ozkan
Early and accurate detection of distributed bearing faults is essential to prevent equipment failures and reduce downtime in industrial environments. This study explores the optimal selection of input signal sources for high-accuracy distributed fault classification, employing wavelet transform and machine learning algorithms. The primary contribution of this work is to demonstrate that robust distributed bearing fault diagnosis can be achieved through optimal sensor fusion and wavelet-based feature engineering, without the need for deep learning or high-dimensional inputs. This approach provides interpretable, computationally efficient, and generalizable fault classification, setting it apart from most existing studies that rely on larger models or more extensive data. All experiments were conducted in a controlled laboratory environment across multiple loads and speeds. A comprehensive dataset, including three-axis vibration, stray magnetic flux, and two-phase current signals, was used to diagnose six distinct bearing fault conditions. The wavelet transform is applied to extract frequency-domain features, capturing intricate fault signatures. To identify the most effective input signal combinations, we systematically evaluated Random Forest, XGBoost, and Support Vector Machine (SVM) models. The analysis reveals that specific signal pairs significantly enhance classification accuracy. Notably, combining vibration signals with stray magnetic flux consistently achieved the highest performance across models, with Random Forest reaching perfect test accuracy (100%) and SVM showing robust results. These findings underscore the importance of optimal source selection and wavelet-transformed features for improving machine learning model performance in bearing fault classification tasks. While the results are promising, validation in real-world industrial settings is needed to fully assess the method's practical reliability and impact on predictive maintenance systems.