Permissible Area Analyses of Measurement Errors with Required Fault Diagnosability Performance

Area of Science:

• Systems Engineering
• Control Theory
• Signal Processing

Background:

• Fault diagnosability is crucial for reliable fault diagnosis.
• Existing quantitative evaluation methods are often inadequate due to measurement errors.
• Improving fault diagnosability enhances overall system safety.

Purpose of the Study:

• To propose a novel method for quantitative fault diagnosability evaluation that accounts for measurement errors.
• To improve the accuracy and reliability of fault diagnosis systems.
• To enhance system safety by mitigating the impact of measurement uncertainties.

Main Methods:

• A permissible area analysis of measurement errors is developed for quantitative fault diagnosability evaluation.
• Kullback-Leibler divergence (KLD) is utilized to design the permissible error area, ensuring residuals follow a normal distribution.
• Sparse kernel density estimation and the Monte Carlo method are employed to address challenges in KLD calculation.

Main Results:

• The proposed method provides a more accurate quantitative evaluation of fault diagnosability under measurement errors.
• The Kullback-Leibler divergence based permissible area design effectively handles measurement uncertainties.
• The feasibility and validity of the method are demonstrated through a practical case study.

Conclusions:

• The developed permissible area analysis method offers a robust approach to fault diagnosability evaluation in the presence of measurement errors.
• This method significantly improves the accuracy of quantitative fault diagnosability assessment.
• The findings contribute to enhanced system safety and reliability in fault diagnosis applications.