Managing Alarms is the Key to Success with Predictive Maintenance..
The Amazon Monitron failure alarm system is well designed to capture the power of AI/ML for quality predictions while overcoming the challenges inherent to all these systems. The challenge is the result of the variability of the underlying data used to train the ML models then produce accurate results based on new data with its own variabilities.
WHY IT MATTERS: All Predictive Maintenance (PdM) systems experience this challenge regardless of the underlying AI-based algorithmic technique or python library used. Managing this variabiltiy is the key to success, and this involves combining the AI generated alarms with human technicians making the final decisions with addtional data. Managing variability and it is the human interpretation that makes alarm management as much art as science.
Alarm problem categories - the job of alarm mangement is also complicated by :
Not all alarms are failure related, so they can be triggering a valid anomaly not related to the use case.
Valid alarms can easily be generated 30 days in advance of actual repair requirement.
In the early weeks of a valid alarm, causality can be undetectable, so it is diffcult to determine if it is valid.
The Monitron alarm system has been setup to facilitate productive decision making in this art and science. The first step is understanding how alarms states flow through the Monitron system and how they drive the art and science of decision making.
NOTE: Use the Monitron online documentation as your primary reference. See Amazon Documentation: Understanding warnings and alerts. The following tips are meant to be supplemental to the Monitron documentation.
Alarms Show in the Monitron Dashboard - they are the key indicator to alert technicians to a possible anomaly. Alarms must be validated by supporting data.
Sample Monitron Mobile Dashboards Showing Alarm States
Alarm States – The following table describes the alarm status for each sensor. In Monitron, sensors are assigned to a position on the asset.
The sensor indicates this position on the asset is Healthy. All measured values are within their normal range, no alarms have been triggered
A warning has been triggered by the sensor at this position indicating early signs of a potential failure condition. We recommend that you monitor the equipment closely and initiate an investigation during an upcoming planned maintenance event.
An alarm has been triggered by the sensor, indicating that the machine vibration or temperature is out of the normal range. We recommend investigating the issue at the earliest opportunity. An equipment failure might occur if the issue isn't addressed.
The alarm state of the sensor at this position has been acknowledged by a technician, but not yet completed. It signifies to the maintenance team that a more formalized investigation will begin.
Changing Alarm States in Dashboard – The Monitron dashboard is used to manage the state of alarms.
To confirm that you are aware of the alarm issue, choose Acknowledge to place the alarm in maintenance state.
After an abnormality has been acknowledged and repaired, Resolve the issue in the mobile app.
Alarm State Progression – Changes to alarm states occur in two ways:
Automated Changes – Steps 1& 2 – these alarms are generated and managed by the platform. For Monitron, alarm states are set by ISO Vibration standards, machine learning algorithms for vibration and temperature. Decisions are based on inspections shown in the yellow highlighted area.
Technician Changes – Steps 3&4 – they are managed by a human maintenance technician. In the Monitron dashboard, the Acknowledge button changes the alarm state from Warning / Alarm to Maintenance. The Resolve Alarm button closes out the Maintenance alarm state and changes it back to Healthy. Decisions are based on diagnostics inspections shown in the blue highlighted area.
Part 2 of this topic will provide more detail with logic diagram on Step 3 above.
NEXT POST: Manging Alarms for Best Results – Part 2
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