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2000
Volume 15, Issue 2
  • ISSN: 2210-3279
  • E-ISSN: 2210-3287

Abstract

The computation of the envelope spectrum of vibration signals is a crucial aspect of vibration analysis and machinery diagnostics, enabling engineers to extract valuable information about the dynamic behavior of mechanical systems. This study provides an overview of various methods and techniques employed to compute the envelope spectrum, including the Hilbert transform, analytic signal processing, and demodulation techniques. The Hilbert transform is a powerful mathematical tool that produces the analytic representation of a signal, allowing for the extraction of the instantaneous amplitude envelope. Analytic signal processing techniques leverage the Hilbert transform to compute the analytic signal, which provides a complex-valued representation of the original signal, facilitating envelope extraction. Demodulation techniques involve the multiplication of the vibration signal by a high-pass filtered version of itself to suppress high-frequency components, leaving behind only the slowly varying envelope. By employing these methods and techniques, engineers can effectively analyze vibration signals, identify amplitude modulations, detect modulation sidebands, and diagnose faults in machinery and structural components. This study aims to provide a comprehensive understanding of envelope spectrum computation methods, offering insights into their theoretical foundations, practical applications, and prospects in vibration analysis and machinery diagnostics.

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2025-09-14

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/content/journals/swcc/10.2174/0122103279326563240725095526
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Concept and Application of Envelope Spectrum Analysis in the Field of Vibration Signal Processing
Int J Sensors Wirel Commun Control 15, 133 (2025); https://doi.org/10.2174/0122103279326563240725095526
/content/journals/swcc/10.2174/0122103279326563240725095526
/content/journals/swcc/10.2174/0122103279326563240725095526
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  • Article Type:     Review Article
Keyword(s): analytic signal processing; demodulation techniques; Envelope spectrum; Hilbert transform; machinery diagnostics; vibration signals

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