Which practice reduces false alarms in sensor data?

Which practice reduces false alarms in sensor data?

• A. Filtering, cross-check across sensors, and ROE thresholds.
• B. Increasing the sensor gain indefinitely.
• C. Turning off all sensors.
• D. Ignoring false alarms to maintain pace.

Answer: (A)

Reducing false alarms in sensor data comes from making the detection more selective and reliable. Filtering cleans up noisy or errant data so random spikes don’t look like real events. Cross-checking across sensors, or using multiple sensors to confirm an event, helps ensure that a true event is supported by independent sources rather than a single noisy channel. Applying ROE thresholds sets clear criteria for when an alert should be triggered, requiring a sufficient level of evidence before action is taken. Increasing sensor gain indefinitely just amplifies both real signals and noise, which actually increases false alarms. Turning off all sensors eliminates detection entirely. Ignoring false alarms doesn’t reduce their occurrence and can undermine safety and performance.

Reducing false alarms in sensor data comes from making the detection more selective and reliable. Filtering cleans up noisy or errant data so random spikes don’t look like real events. Cross-checking across sensors, or using multiple sensors to confirm an event, helps ensure that a true event is supported by independent sources rather than a single noisy channel. Applying ROE thresholds sets clear criteria for when an alert should be triggered, requiring a sufficient level of evidence before action is taken.

Increasing sensor gain indefinitely just amplifies both real signals and noise, which actually increases false alarms. Turning off all sensors eliminates detection entirely. Ignoring false alarms doesn’t reduce their occurrence and can undermine safety and performance.