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Automatic Meter Reading

SCHAD AMR builds on our automation connectivity to populate other external systems, such as IBM Maximo and IBM TRIRIGA. AMR is also able to publish data directly into IBM's Watson IOT Platform.
 
The volume of data in an automation network - the number of connected data points, and the number of value changes in a given period - can be huge. Transferring data value changes directly can easily overload a target system without providing great value. AMR supplies rules designed to filter out changes that do not contribute to decision-making. Different data transfer rates can be set depending on the value. Processing rules can detect peaks, troughs, calculate averages in a period, or convert numbers to discrete values.
 

For example, if a temperature reading is considered normal between 0 and 100 degress, we may simply want to record the peak value very 24 hours. However, once the temperature exceeds 100 degress, we record it every 10 minutes so that we can take action if it continues to rise. Alternatively, we could convert the underlying value into NORMAL, HIGH or OVERTEMP values, based on a set of thresholds.

 

Condition-Based Maintenance

 

There is a great body of research (search for 'asset failure patterns') that has concluded that only around 20% of failures relate to time. The other 80% are categorised as 'random'. However, once you start to analyse the behaviour of an asset leading up to a failure, patterns emerge that show that these failures are not random at all, and many can accurately be predicted by watching key behavioural characteristics of the asset. Scheduling maintenance based on observed data values from the equipment is called Condition-Based Maintenance (CBM).

 
CBM has the potential to save billions of dollars by: reducing downtime through better preventative maintenance; reducing the cost of unnecessary maintenance activities; ensuring that parts are only replaced when necessary.
 
What's more, it is easy to start implementing a CBM strategy. It only needs a few data points to be identified which can help influence the maintenance schedule to demonstrate a rapid return on investment. For example, one pharmaceutical company started to measure the air pressure differential on two sides of their hepa-filters. By only changing the filters when the sensors showed a certain level of pressure-drop, they saved $350,000 in one year. There are many examples of a simple change in maintenance scheduling to include asset data can generate an ROI many times over.
 
Most organisations already have the data points they need in their automation or building-management system. However, these values are siloed and not integrated into the systems that manage the maintenance schedules and activities.
 
AMR provides a foundation for moving from basic calender-based maintenance to Condition-Based Maintenance (CBM). AMR's rules ensure that key data value changes are transmitted to maintenance systems, either as data point changes for use with Condition Monitoring applications, or as Work Orders or Work tasks.

 

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