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Updated: Oct 13, 2025

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition
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Non-Singleton Type-3 Fuzzy Approach for Flowmeter Fault Detection: Experimental Study in a Gas Industry.

Jing-He Wang1, Jafar Tavoosi2, Ardashir Mohammadzadeh3

  • 1School of Economics and Finance, Huaqiao University, Quanzhou 362021, China.

Sensors (Basel, Switzerland)
|November 13, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an optimized non-singleton type-3 fuzzy logic system (NT3 FLS) for accurate flowmeter fault detection. The novel approach effectively models signals with uncertainties, improving fault identification in industrial settings.

Keywords:
correntropy Kalman filterfault detectionlearning algorithmnon-Gaussian noisetype-3 fuzzy logic

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Area of Science:

  • Engineering
  • Control Systems
  • Artificial Intelligence

Background:

  • Flowmeters are critical in the gas industry, but prone to faults due to measurement errors and uncertainties.
  • Existing fault detection methods struggle with high levels of noise and non-Gaussian disturbances common in industrial applications.

Purpose of the Study:

  • To develop a robust flowmeter fault detection approach using optimized non-singleton type-3 fuzzy logic systems (NT3 FLS).
  • To enhance the modeling of signals affected by significant measurement errors and uncertainties.
  • To improve the accuracy and reliability of fault detection in industrial flowmeters.

Main Methods:

  • Implementation of NT3 FLS for system modeling and fault detection on an experimental gas industry plant.
  • Utilizing an NT3 FLS with uncertain footprint-of-uncertainty (FOU), fuzzy secondary memberships, and adaptive non-singleton fuzzification for noise and error handling.
  • Tuning NT3 FLS parameters using maximum correntropy (MC) unscented Kalman filter (KF) and learning rule parameters with correntropy KF (CKF) for handling non-Gaussian noises.

Main Results:

  • The developed NT3 FLS demonstrated powerful signal modeling capabilities even in the presence of noise and errors.
  • The optimized NT3 FLS effectively detected various common flowmeter faults with good accuracy.
  • The proposed learning algorithms successfully handled non-Gaussian noises inherent in industrial applications.

Conclusions:

  • The optimized NT3 FLS provides a powerful and accurate method for flowmeter fault detection in challenging industrial environments.
  • The approach offers superior performance compared to conventional methods, especially in systems with high measurement errors and uncertainties.
  • This research contributes a significant advancement in intelligent fault diagnosis for industrial instrumentation.