Noise Reduction of Steam Trap Based on SSA-VMD Improved Wavelet Threshold Function

Area of Science:

• Mechanical Engineering
• Signal Processing
• Acoustics

Background:

• Steam trap performance is critical for efficient steam system operation and energy conservation.
• Monitoring steam trap condition via acoustic emission signals is vital for identifying internal leakage.
• Environmental noise interferes with accurate acoustic signal analysis for steam trap diagnostics.

Purpose of the Study:

• To develop an effective noise reduction method for acoustic emission signals from steam traps.
• To enhance the accuracy of identifying internal leakage states in steam traps despite background noise.
• To improve the signal-to-noise ratio for more reliable steam trap condition monitoring.

Main Methods:

• A novel denoising method combining Sparrow Search Algorithm (SSA), Variational Modal Decomposition (VMD), and improved wavelet thresholding.
• SSA optimized VMD parameters (decomposition level K, penalty factor α) using minimum envelope entropy.
• Wavelet threshold denoising applied to VMD-decomposed modes, followed by signal reconstruction.

Main Results:

• The proposed SSA-VMD method effectively filtered background noise from acoustic emission signals.
• The technique significantly increased the signal-to-noise ratio compared to traditional methods.
• Reduced root-mean-square error demonstrated superior noise reduction performance for steam trap signals.

Conclusions:

• The integrated SSA-VMD and wavelet thresholding approach offers a robust solution for denoising steam trap acoustic signals.
• This method enhances the reliability of acoustic emission-based diagnostics for steam trap internal leakage.
• The findings contribute to improved energy efficiency and operational integrity in steam systems.