Reliable bearing fault diagnosis is essential for predictive maintenance of rotating machinery, particularly in applications where contact-based vibration sensors are difficult to install or maintain. This study proposes an explainable acoustic fault diagnosis framework based on Mel-spectrogram representations and a hybrid Convolutional Neural Network–Gated Recurrent Unit (CNN–GRU) architecture. Acoustic emission signals collected from a controlled bearing fault simulator are first segmented and transformed into Mel-spectrograms to represent the time–frequency structure of normal and faulty bearing conditions. The CNN module extracts localized spectral patterns from the Mel-spectrogram images, while the GRU module models temporal dependencies along the spectrogram time axis with fewer recurrent parameters than conventional LSTM-based designs. To address the interpretability limitations of deep learning models, Grad-CAM, Integrated Gradients, and SHAP are employed to analyze the frequency–time regions contributing to the model decisions. In addition, the explanation maps are compared with fault-relevant spectral regions derived from fault characteristic frequency analysis to evaluate whether the model focuses on physically meaningful patterns rather than arbitrary image regions. The proposed framework achieved an high accuracy of in the initial experiment and was further evaluated through repeated validation to assess performance stability under small-sample conditions. The results demonstrate that acoustic sensing combined with explainable CNN–GRU learning can provide a non-contact and interpretable alternative for bearing fault diagnosis. However, the limited dataset size remains an important constraint, and future studies should validate the framework on larger record-level and cross-domain datasets.
Emrah Aslan, Yıldırım Özüpak· Proceedings of the Instituti...· 1 citation
Electricity demand forecasting is crucial for effective grid operation, planning, and decision-making. This study presents a comparison between classical time series models—AutoRegressive Integrated Moving Average (ARIMA) and Seasonal AutoRegressive Integrated Moving Average (SARIMA)—and a deep learning–based method, Gated Recurrent Units (GRU), for forecasting hourly electricity demand. The models are tested on a real-world dataset, enriched with weather and calendar variables to capture temporal and exogenous effects on electricity consumption. To ensure a fair and reproducible comparison, all models are trained and evaluated in a common experimental framework, including a well-defined chronological train-test split and rolling-origin (walk-forward) validation strategy. The forecasting performance is evaluated for short-term (24 h) and medium-term (168 h) horizons using standard error metrics, namely, root mean squared error, mean absolute error, and Mean Absolute Percentage Error (MAPE). The results of the experiment demonstrate that the GRU model performs better than ARIMA and SARIMA models especially for longer forecasting horizons due to its capability to learn nonlinear relationships and long-term temporal dependencies. The GRU approach gives better forecasting accuracy in the case of complex demand dynamics, but linear seasonal patterns can still be modeled by classical statistical models. The aim of this study is not to directly detect or predict system failures, nor does it depend on explicit fault or outage data. Its main contribution is instead in improving the accuracy of electricity demand forecasting, which can indirectly assist preventive grid operation and planning by reducing the uncertainty in expected load profiles.
Received: 7 August 2025 | Revised: 20 March 2026 | Accepted: 23 June 2026
Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.
Data Availability Statement
The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/saurabhshahane/electricity-load-forecasting.
Author Contribution Statement
Emrah Aslan: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Yıldırım Özüpak: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision. Feyyaz Alpsalaz: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision. Hasan Uzel: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision.
Emrah Aslan, Yıldırım Özüpak, Feyyaz Alpsalaz et al.· Artificial Intelligence and...· 0 citations
This study investigates the application of deep learning architectures, including Convolutional Neural Network, VGG16, VGG19, ResNet50, and MobileNet, for brain tumor detection and classification and confirms that advanced deep learning architectures not only achieve high classification accuracy but also improve interpretability, thereby offering reliable and clinically applicable solutions for automated brain tumor diagnosis.
H. Uzel, Feyyaz Alpsalaz, Yıldırım Özüpak et al.· Computers and Electronics in...· 0 citations
An advanced diagnostic pipeline is proposed that transforms one-dimensional time-series current and voltage signals into informative two-dimensional spatial representations using Gramian angular field encoding and Coati optimization algorithm-optimized transfer learning framework provides an accurate, interpretable, and computationally feasible solution for induction motor fault diagnosis in electric vehicle applications.
Yıldırım Özüpak, Emrah Aslan· Proceedings of the Instituti...· 1 citation