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Related Experiment Videos

Optimal filtering and Bayesian detection for friction-based diagnostics in machines.

L R Ray1, J R Townsend, A Ramasubramanian

  • 1Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA. laura.ray@dartmouth.edu

ISA Transactions
|August 23, 2001
PubMed
Summary

This study introduces a novel method combining non-model and model-based approaches for detecting process variations. The technique accurately estimates friction torque in rotating machines, enabling early fault diagnosis.

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Area of Science:

  • Engineering
  • Control Systems
  • Machine Diagnostics

Background:

  • Non-model-based diagnostic methods often struggle with indirect signal interpretation.
  • Accurate process behavior analysis is crucial for fault detection in dynamic systems.

Purpose of the Study:

  • To develop an integrated non-model and model-based approach for detecting process variations.
  • To apply this method for friction estimation and diagnosis in rotating machinery.

Main Methods:

  • Utilizes nonlinear filtering and maximum likelihood hypothesis testing.
  • Employs a nonlinear observer to estimate friction torque from shaft position and motor input voltage.
  • Compares estimated friction torque with a process model for fault diagnosis.

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Main Results:

  • Successfully estimated friction torque in a rotating machine.
  • Demonstrated the ability to detect statistically significant changes in friction characteristics due to load variations.
  • Validated the method's effectiveness across a range of friction behaviors.

Conclusions:

  • The integrated approach provides a direct variable (friction torque estimate) for diagnosing model variations or faults.
  • This method enhances the reliability of diagnostics in dynamic systems with poorly known inputs.
  • Experimental results confirm the capability to detect friction anomalies under varying operational conditions.