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Related Experiment Video

Updated: Feb 3, 2026

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
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Process Pattern-Based Near-Infrared Spectroscopy (NIRS) Fault Detection Using a Potential Function.

Niannian Zheng1, Xiaoli Luan1, Fei Liu1

  • 1Key Laboratory for Advanced Process Control of Light Industry of the Ministry of Education, Institute of Automation, Jiangnan University, Wuxi, China.

Applied Spectroscopy
|October 24, 2018
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Summary
This summary is machine-generated.

This study introduces a novel near-infrared (NIR) fault detection technology using process patterns and potential functions. This method enhances industrial system monitoring by providing faster, more accurate, and visually clear fault identification.

Keywords:
Near-infrared spectroscopyelastic net-PCAfault detectionpotential functionprocess pattern

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Area of Science:

  • Analytical Chemistry
  • Process Control Engineering
  • Spectroscopy

Background:

  • Industrial processes require robust fault detection for safety and efficiency.
  • Traditional methods may lack the sensitivity to detect subtle process deviations.
  • Near-infrared (NIR) spectroscopy offers molecular-level insights into process dynamics.

Purpose of the Study:

  • To propose a new fault detection technology for industrial systems using near-infrared (NIR) spectroscopy.
  • To introduce and develop the concept of a 'process pattern' for enhanced process monitoring.
  • To apply potential function discrimination for accurate classification of process states.

Main Methods:

  • Utilizing near-infrared (NIR) spectroscopy to gather molecular-level process data.
  • Developing a process pattern construction method based on elastic net-Principal Component Analysis (PCA).
  • Employing a potential function discriminant method for classifying process patterns and identifying system states.

Main Results:

  • The proposed method successfully distinguished and classified process patterns in crude oil desalination and dehydration.
  • Achieved nonlinear process classification with improved fault detection accuracy and visualization.
  • Demonstrated faster and more accurate fault detection compared to existing methods.

Conclusions:

  • The NIR-based process pattern and potential function method offers superior fault detection capabilities.
  • This approach provides a more comprehensive understanding of essential process features from a molecular perspective.
  • The technology enables enhanced accuracy, speed, and visualization in industrial process monitoring.