Fault Detection for a Class of Uncertain Sampled-Data Systems Using Deterministic Learning.

Area of Science:

• Control Engineering
• System Identification
• Machine Learning

Background:

• Nonlinear sampled-data systems present challenges in fault diagnosis due to unmodeled dynamics.
• Accurate fault detection is crucial for maintaining system reliability and safety.

Purpose of the Study:

• To develop a learning-based fault diagnosis approach for nonlinear sampled-data systems.
• To enhance fault detection speed and accuracy by effectively utilizing learned system dynamics.
• To analyze the impact of system uncertainties on fault diagnosis performance.

Main Methods:

• Deterministic learning method to acquire unmodeled sampled dynamics.
• Constant neural networks to store normal and fault pattern dynamics.
• Fault detection scheme utilizing learned knowledge recall for rapid response.
• Derivation of analytical results for fault detection conditions and time.
• Development of the 'duty ratio' concept to analyze mismatch function effects.
• Design of an extraction operator to capture mismatch function features.

Main Results:

• The mismatch function significantly influences the performance of the diagnosis scheme.
• The proposed method demonstrates improved fault diagnosis performance.
• Analytical results provide conditions and timeframes for effective fault detection.
• Simulation studies validate the effectiveness of the learning-based approach.

Conclusions:

• The learning-based fault diagnosis approach offers a robust solution for nonlinear sampled-data systems.
• Effective utilization of learned dynamics and mismatch function analysis are key to improved diagnostic performance.
• The developed method provides a framework for enhanced system monitoring and fault management.