What is a simple method for fault detection in a closed-loop control system?

What is a simple method for fault detection in a closed-loop control system?

• A. Rely on periodic manual testing of the plant.
• B. Ignore small deviations to avoid alarm fatigue.
• C. Use residual-based detection by comparing measured output with model-predicted output; trigger fault when discrepancy exceeds threshold.
• D. Monitor only the actuator current and ignore sensor data.

Answer: (C)

In fault detection for a closed-loop system, comparing what you measure to what the model predicts is a powerful and straightforward approach. This residual-based method works because you have a mathematical description of how the plant should respond to the known inputs and states. The difference between the measured output and the model’s predicted output—the residual—should stay small when everything is functioning normally, as sensor noise and small modeling errors are the only things causing tiny discrepancies. When a fault occurs, such as a sensor drifting, an actuator losing gain, or a change in the plant dynamics, the actual output will diverge from the model’s prediction. That divergence shows up as a larger residual. By setting a threshold, the system can automatically trigger a fault alarm once the residual exceeds that limit, enabling real-time detection without waiting for manual checks. This approach is practical to implement in software and can run continuously alongside the control loop, with thresholds that balance quick detection against robustness to noise. Other methods fall short in real-time fault detection. Relying on periodic manual testing provides only intermittent checks and can miss faults that develop between tests. Ignoring small deviations sacrifices sensitivity and can let incipient faults slip by until performance degrades noticeably. Monitoring only actuator current ignores faults in sensors and other parts of the process, giving an incomplete view of system health. So, residual-based detection—using the difference between measured and model-predicted outputs with a threshold—offers timely, reliable fault detection in a closed-loop system.

In fault detection for a closed-loop system, comparing what you measure to what the model predicts is a powerful and straightforward approach. This residual-based method works because you have a mathematical description of how the plant should respond to the known inputs and states. The difference between the measured output and the model’s predicted output—the residual—should stay small when everything is functioning normally, as sensor noise and small modeling errors are the only things causing tiny discrepancies.

When a fault occurs, such as a sensor drifting, an actuator losing gain, or a change in the plant dynamics, the actual output will diverge from the model’s prediction. That divergence shows up as a larger residual. By setting a threshold, the system can automatically trigger a fault alarm once the residual exceeds that limit, enabling real-time detection without waiting for manual checks. This approach is practical to implement in software and can run continuously alongside the control loop, with thresholds that balance quick detection against robustness to noise.

Other methods fall short in real-time fault detection. Relying on periodic manual testing provides only intermittent checks and can miss faults that develop between tests. Ignoring small deviations sacrifices sensitivity and can let incipient faults slip by until performance degrades noticeably. Monitoring only actuator current ignores faults in sensors and other parts of the process, giving an incomplete view of system health.

So, residual-based detection—using the difference between measured and model-predicted outputs with a threshold—offers timely, reliable fault detection in a closed-loop system.