Monitron ML Alarm Thresholds

Unfortunately, Amazon Monitron does not provide a way to see the exact numerical values of the new alarm thresholds after they have been adjusted due to a "No failure detected" feedback.

The system uses machine learning algorithms to continuously adapt and fine-tune its baseline understanding of what's "normal" for each specific asset. When you provide feedback that an alert was a false positive by selecting "No failure detected (mute alert)", Monitron adjusts its thresholds accordingly. However, these adjustments are made internally by the machine learning model and are not exposed to users as specific numerical values.

It's important to note that even when ISO threshold-based notifications are muted in this way, Monitron will continue to use its machine learning capabilities to detect potential failures. This means the system is still monitoring for abnormalities, but with an adjusted understanding of what constitutes "normal" operation for that particular asset.

While you can't see the exact numerical changes in the thresholds, you can observe the overall effect of these adjustments by monitoring the frequency and nature of subsequent alerts for that asset. If you're seeing fewer false positives after providing this feedback, it indicates that the system has successfully adjusted its thresholds based on your input.

If you need more detailed insights into the specific conditions of your assets, you may want to consider using the sensor data visualization features in the Monitron app. While this won't show you the exact alarm thresholds, it can give you a better understanding of the asset's performance over time and how it relates to the alerts you're receiving.
Sources
Understanding sensor measurements - Amazon Monitron
Understanding warnings and alerts - Amazon Monitron
Step 5: Muting and unmuting alerts - Amazon Monitron