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Area of Science:

  • Instrumentation and Measurement
  • Data Science
  • Control Systems Engineering

Background:

  • Board channels are critical links between data acquisition systems and plant sensors.
  • Flawed board channels can lead to poor-quality or erroneous data, compromising operational strategies.
  • Accurate detection of board channel status is essential for maintaining system reliability.

Purpose of the Study:

  • To propose a data-driven approach for detecting the status of enclosed board channels.
  • To develop a method for constructing critical faulty data for training detection models.
  • To validate the effectiveness of the proposed detection method using experimental data.

Main Methods:

  • Utilized an error time series derived from multiple excitation signals and internal register values.
  • Constructed critical faulty data using a null matrix with maximum projection, alongside healthy data, for training.
  • Employed a well-trained probabilistic neural network for validating the board channel status.

Main Results:

  • The proposed data-driven approach successfully detected the status of the enclosed board channel.
  • The method demonstrated effectiveness in distinguishing between healthy and faulty channel data.
  • Experimental validation confirmed the reliability of the probabilistic neural network in status assessment.

Conclusions:

  • The developed data-driven method provides an effective means for monitoring board channel integrity.
  • The technique enhances data quality by identifying and mitigating issues from faulty sensor channels.
  • This approach contributes to improved reliability and accuracy in plant data acquisition systems.