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<p>This study presents a comprehensive investigation into the application of vibration sensors coupled with advanced signal processing techniques for the diagnosis and maintenance of mechanical systems. The core of the research is centered around the integration of several signal processing techniques, namely filtering, the Fourier transform, wavelet analysis, decimation/interpolation, and spectral analysis. Each of these methods contributes uniquely to improving data quality, isolating noise, and extracting significant diagnostic features from the raw sensor signals. Filtering eliminates irrelevant frequency components, while the Fourier transform provides a global frequency overview. Wavelet analysis offers multi-resolution insight into transient signal behavior, and decimation/interpolation helps in optimizing signal resolution. Spectral analysis is further used to isolate characteristic frequencies associated with specific faults. A key contribution of this study is the quantitative evaluation of signal enhancement through these methods. The proposed signal processing framework resulted in a signal-to-noise ratio (SNR) improvement of 20 dB and enabled fault detection with an accuracy exceeding 90%, demonstrating the effectiveness of the approach in real-world scenarios. Moreover, the study bridges the gap between theoretical signal processing and practical maintenance needs by validating the techniques on experimental data acquired from a centrifugal pump testbed. This not only confirms the applicability of the methods but also highlights their potential for real-time condition monitoring in industrial environments. By effectively combining multiple signal processing tools, the study underscores a significant improvement in the early detection of faults, reduction of false alarms, and enhancement of maintenance planning. These results contribute meaningfully to the advancement of predictive maintenance strategies, allowing for proactive intervention, reduced downtime, and improved operational reliability of mechanical systems in demanding industrial settings.</p>

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Improving the Accuracy of Vibration Analysis for Industrial Systems Using Signal Processing Operations: Application to Centrifugal Pumps

  • 期刊论文

  • 发表日期：

    2025年5月27日

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  • Zine Ghemari,
  • Salah Belkhiri,
  • Salima khaoula Reguieg

摘要

This study presents a comprehensive investigation into the application of vibration sensors coupled with advanced signal processing techniques for the diagnosis and maintenance of mechanical systems. The core of the research is centered around the integration of several signal processing techniques, namely filtering, the Fourier transform, wavelet analysis, decimation/interpolation, and spectral analysis. Each of these methods contributes uniquely to improving data quality, isolating noise, and extracting significant diagnostic features from the raw sensor signals. Filtering eliminates irrelevant frequency components, while the Fourier transform provides a global frequency overview. Wavelet analysis offers multi-resolution insight into transient signal behavior, and decimation/interpolation helps in optimizing signal resolution. Spectral analysis is further used to isolate characteristic frequencies associated with specific faults. A key contribution of this study is the quantitative evaluation of signal enhancement through these methods. The proposed signal processing framework resulted in a signal-to-noise ratio (SNR) improvement of 20 dB and enabled fault detection with an accuracy exceeding 90%, demonstrating the effectiveness of the approach in real-world scenarios. Moreover, the study bridges the gap between theoretical signal processing and practical maintenance needs by validating the techniques on experimental data acquired from a centrifugal pump testbed. This not only confirms the applicability of the methods but also highlights their potential for real-time condition monitoring in industrial environments. By effectively combining multiple signal processing tools, the study underscores a significant improvement in the early detection of faults, reduction of false alarms, and enhancement of maintenance planning. These results contribute meaningfully to the advancement of predictive maintenance strategies, allowing for proactive intervention, reduced downtime, and improved operational reliability of mechanical systems in demanding industrial settings.