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Updated: Jun 5, 2026

Monitoring the Wall Mechanics During Stent Deployment in a Vessel

Monitoring the Wall Mechanics During Stent Deployment in a Vessel

Published on: May 8, 2012

Successive ridge detection framework: A novel method for valve stiction detection.

Xun Lang¹, Songhua Liu¹, Jiande Wu²

¹School of Information, Yunnan University, Kunming, 650091, Yunnan, China.

ISA Transactions

|June 3, 2026

Summary

This summary is machine-generated.

This study introduces a novel ridge detection framework (SRDF) to accurately identify valve stiction in industrial control loops. SRDF offers robust and automatic detection, outperforming existing methods.

Keywords:

Control-loop oscillation diagnosis Process monitoring Successive ridge detection Valve stiction detection

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Area of Science:

Control Engineering
Signal Processing
Industrial Automation

Background:

Valve stiction is a common cause of oscillations in industrial control loops.
Existing nonlinear-based stiction detection methods suffer from mode mixing and noise interference.

Purpose of the Study:

To introduce a novel and robust method for automatic valve stiction detection.
To overcome the limitations of traditional signal decomposition techniques.

Main Methods:

Introduced ridge detection (RD) to track instantaneous frequencies on STFT spectrograms.
Developed a successive RD framework (SRDF) using Hodrick-Prescott filtering and spectral standard deviation for ridge extraction.
Established STFT parameter selection rules for reliable ridge detection.

Main Results:

SRDF achieved high detection accuracies: 88.46% on the International Stiction Database and 85.00% on an industrial dataset.
The proposed method effectively mitigates mode mixing and suppresses pseudo-ridges.
SRDF demonstrated superior performance compared to advanced decomposition-based methods and baseline RD.

Conclusions:

The successive ridge detection framework (SRDF) provides an effective and practical solution for automatic valve stiction detection.
RD offers advantages over traditional methods by avoiding signal decomposition and mitigating mode mixing.
The findings highlight the potential for improved industrial process control through enhanced stiction detection.