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Related Experiment Videos

Winner take all experts network for sensor validation.

G G Yen1, W Feng

  • 1Intelligent Systems and Control Laboratory, School of Electrical and Computer Engineering, Oklahoma State University, Stillwater 74078-5032, USA. gyen@ceat.okstate.edu

ISA Transactions
|May 23, 2001
PubMed
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This study introduces a novel Winner Take All Experts (WTAE) neural network for robust sensor validation in industrial settings. The WTAE model efficiently estimates sensor values, enhancing machinery health monitoring and fault detection capabilities.

Area of Science:

  • Industrial Sensor Technology
  • Machine Learning for Industrial Systems
  • Fault Detection and Diagnostics

Background:

  • Sensor validation is critical for industrial equipment operation and control, especially in harsh environments.
  • Deviations in sensor signals require analysis to trigger alarms and ensure accurate measurements.
  • Neural network models offer a promising approach for on-line sensor value estimation using neighboring sensor data.

Purpose of the Study:

  • To develop and validate a computationally efficient neural network model for on-line sensor health estimation.
  • To utilize the discrepancy between measured and predicted sensor values as an indicator of sensor health.
  • To implement a robust fault detection system using analytical redundancy and decision-level fusion.

Main Methods:

Related Experiment Videos

  • Implementation of a Winner Take All Experts (WTAE) network utilizing a 'divide and conquer' strategy.
  • Employing a growing fuzzy clustering algorithm to decompose complex problems into simpler sub-problems.
  • Comparison of estimated sensor values with real readings in both time and frequency domains, using three fault indicators for decision-level fusion.

Main Results:

  • The proposed WTAE network significantly reduces computational time for neural network training.
  • Simulations demonstrated the WTAE network's effectiveness in detecting sensor failures.
  • The WTAE approach proved competitive with or superior to existing sensor validation methods.

Conclusions:

  • The WTAE network provides an efficient and effective method for on-line sensor validation in industrial applications.
  • The combination of fuzzy clustering and neural networks enhances sensor health monitoring accuracy.
  • The proposed method offers a reliable approach to analytical redundancy for improved sensor fault detection.