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Mechanical Damage Assessment for Pneumatic Control Valves Based on a Statistical Reliability Model.

Nirbhay Mathur1, Vijanth Sagayan Asirvadam2, Azrina Abd Aziz2

  • 1Center for System Engineering, Institute of Autonomous Systems, Univiersiti Teknologi PETRONAS, Seri Iskander 32610, Perak, Malaysia.

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Summary
This summary is machine-generated.

This study assessed statistical models for processing plant reliability, finding the Weibull distribution best predicts failures like control valve stiction. This aids in optimizing plant operations and safety.

Keywords:
control valvefault detectionreliabilityvisualization

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Area of Science:

  • Industrial Engineering
  • Reliability Engineering
  • Statistical Modeling

Background:

  • Processing plants rely on numerous interconnected loops and instruments, necessitating robust reliability assessments.
  • Existing statistical models are crucial for visualizing and predicting operational availability and potential failures.
  • Understanding failure modes is essential for maintaining plant integrity and safety.

Purpose of the Study:

  • To evaluate and visualize the performance of various statistical reliability models (normal, log-normal, exponential, Weibull).
  • To optimize these models using Maximum Likelihood Estimation (MLE).
  • To identify the most suitable model for predicting failures in simulated process control operations.

Main Methods:

  • Comparison of normal, log-normal, exponential, and Weibull distributions for reliability assessment.
  • Optimization of statistical models using Maximum Likelihood Estimation (MLE).
  • Simulation of control valve stiction failure under various flow rates using a pilot plant.

Main Results:

  • The Weibull distribution demonstrated superior performance in modeling the simulated control valve stiction failure.
  • MLE effectively optimized the parameters of the evaluated statistical distributions.
  • The pilot plant simulation provided a practical validation of the statistical models.

Conclusions:

  • The Weibull distribution is highly effective for predicting process control failures, specifically stiction in control valves.
  • Reliability assessment using optimized statistical models is vital for processing plant operational management.
  • This research provides a validated approach for enhancing the reliability and safety of industrial processes.