A Novel Fault Detection with Minimizing the Noise-Signal Ratio Using Reinforcement Learning.

Area of Science:

• Engineering
• Control Systems
• Machine Learning

Background:

• Unexpected faults in dynamic systems pose significant risks.
• Robust fault detection is crucial for system reliability and safety.
• Traditional methods may struggle with noisy data and complex system dynamics.

Purpose of the Study:

• To propose a novel reinforcement learning approach for detecting unexpected faults.
• To enhance fault detection robustness by minimizing the noise-signal ratio.
• To quantify the severity of detected faults.

Main Methods:

• A reinforcement learning algorithm is developed for fault detection.
• The method minimizes the noise-signal ratio in data series for improved robustness.
• Fault detection is performed by comparing model forecasts with real-time process data.
• Fault severity is assessed by measuring parameter deviations between healthy and faulty states.

Main Results:

• The proposed reinforcement learning approach effectively detects unexpected faults.
• The method demonstrates robustness against noise in data series.
• The algorithm successfully quantifies fault severity.
• Validation on a DC-motor system confirms the algorithm's effectiveness.

Conclusions:

• Reinforcement learning offers a powerful tool for robust fault detection.
• Minimizing the noise-signal ratio is key to achieving reliable fault identification.
• The developed algorithm provides a practical solution for monitoring dynamic systems like DC motors.