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All Journal PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
OFFOLE, Florence
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Computer Science & IT

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Real-Time Vibration Fault Detection in Rotating Machines Using Transformers to Minimize Production Losses in Industry 5.0: VIBT TOUKAP NONO, FERNAND JOSEPH; TOKOUE NGATCHA , DIANORE; OFFOLE, Florence; Nyatte, Steyve; MOUZONG PEMI, Marcelin
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.566

Abstract

Quickly identifying anomalies in rotating machinery is crucial for safety and profitability in contemporary industry (Industry 5.0). Unidentified failures can cause costly malfunctions and production interruptions. This research proposes an innovative strategy based on Transformer for the analysis of multidimensional vibration events (VIBT), with a view to early and accurate detection of anomalies in rotating machinery. The goal is to minimize production interruptions in Industry 5.0. The study highlights the limitations of conventional vibration analysis approaches and traditional deep learning techniques, emphasizing the need for innovative solutions. VIBT incorporates transformers and a filter bank convolution (FBC) module for effective denoising, as well as an adaptive wavelet transformation (WTA) mechanism for dynamic feature fusion at various scales, thereby addressing the challenges posed by non-stationary and noisy signals. Extensive testing on the Mafaulda dataset reveals that VIBT achieves 98.1% precision and 98.8% accuracy, significantly outperforming existing standard models. The results suggest that VIBT not only improves fault detection capabilities but also optimizes maintenance strategies in industrial applications, paving the way for future research on semi-supervised learning based on transformers and the integration of intermodal data.
Fault Modeling to Determine the Reliability Status of Rotating Machines Using Deep Learning Methods Based on Vibrations from Acoustic Emissions from Cooling Fans TOUKAP NONO, FERNAND JOSEPH; TOKOUE NGATCHA , DIANORE; OFFOLE, Florence; NDI, FRANCELIN; MOUZONG PEMI, Marcelin
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.569

Abstract

Modern industrial production acknowledges the increasing significance of maintenance. As of right now, maintenance is seen as a service that aims to maintain the effectiveness of systems and installations while adhering to quality, energy efficiency, and protection standards. An inventive technique to automate rotating machine maintenance procedures has been created in this study. To identify failures and flaws in the motors through their supports, where the fan blades are attached, a technique based on capturing the noises produced by their cooling fans and utilizing deep learning to diagnose problems was investigated. Two operational circumstances were envisioned: the absence of fault and the presence of fault. The machine is correctly powered and running in ideal circumstances when it is not having any issues. In contrast, failures were gradually created purposefully and then documented in order to better understand the faults. Utilizing a pre-trained network (SqueezeNet) built on the ImageNet database, the convolutional neural network (CNN)-based technique was constructed. Applying transfer learning to the spectrograms obtained from the sound emission recordings of our machine's fan in both working modes demonstrated outstanding performance (accuracy = 0.987), confirming the methodology's outstanding quality.
Co-Authors MOUZONG PEMI, Marcelin NDI, FRANCELIN Nyatte, Steyve TOKOUE NGATCHA , DIANORE TOUKAP NONO, FERNAND JOSEPH