Jul 2026· IEEE International Conference on Circuits and Systems for Communications· pp. 1-6· 0 citations· 17 references
Abstract
Brake fault diagnosis is critical for enhancing the safety and reliability of vehicles. This paper presents an approach of utilizing spectrogram deep learning, where the spectrogram is defined as the representation of the signal in terms of timefrequency plane. To diagnose faults in NVH signals, a novel framework combining deep learning models and unsupervised clustering methods is proposed, especially focusing on spectrograms. Raw multi-channel NVH data are divided into overlapping temporal segments and converted into timefrequency spectrogram images via Short-Time Fourier Transform (STFT). Afterward, intrinsic patterns of vibrations are extracted by K-means clustering, and a two-dimensional convolutional neural network (2D CNN) is trained based on the extracted clusters. Experimental datasets are formed by converting NVH signals into 37,962 spectrogram images through 38 sensor channels. Classification accuracy, precision, recall, and F1-score values reached up to 98.41% and more than 0.98 for all clusters, and the areas under ROC curves are close to 1.0 for all clusters.
Rotary machines are vital in industrial and electrical systems, and prompt defect detection is crucial to prevent operational failures and financial losses. This article presents a framework using a Convolutional Neural Network (CNN) for defect detection via spectrogram images derived from simulated voltage, current, a...
R. M. Vizhi, V. Karthikeyani, L. Sundari et al.· International Conference on...· 0 citations
Electrical current signals provide essential information about the health and performance of electrical systems. Identifying distorted signals is critical for the early detection of faults in electrical systems, and in turn, helps prevent damage, instability, and loss of efficiency. This paper presents a study on class...
M. Shatnawi, Mariam Alsaqqaf, Salihah Almenhali et al.· 2026 6th International Confe...· 0 citations
A hybrid anomaly detection framework for vibroacoustic signals obtained from a DGEN 380 turbofan test bench provides an effective and interpretable solution for anomaly detection in vibroacoustic systems and demonstrates potential for application in predictive maintenance of aeroengines and other rotating machinery.
A Hybrid Knowledge-DL network (HKD-SVM) that utilizes Support Vector Machine (SVM) as the classifier, making the network well-suited for nonlinear, high-dimensional classification scenarios with limited training samples, which are common in power system applications.
The results demonstrate that acoustic sensing combined with explainable CNN–GRU learning can provide a non-contact and interpretable alternative for bearing fault diagnosis, and the limited dataset size remains an important constraint, and future studies should validate the framework on larger record-level and cross-do...
Emrah Aslan, Yıldırım Özüpak· Proceedings of the Instituti...· 1 citation
The availability and reliability of the power transmission system are crucial to ensuring the continuity of the electrical energy supply. Disturbances caused by natural events, vegetation interference, or animal activity can lead to widespread blackouts, necessitating rapid and accurate fault analysis. This study propo...
Hafizh Tri Januar, D. Purwanto, D. Kuswidiastuti· International Seminar on Int...· 0 citations
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