Emerson’s New Asset Monitoring Device Brings Smart Analytics to the Edge

What makes the AMS Asset Monitor different to standard IoT devices and how can edge computing revolutionize industry in general?

Edge Computing

Thanks to the ever-falling price of electronics it is no longer uncommon to find electrical circuitry in everyday objects. Things that used to be boring and mechanical including microwaves and toasters now all include some form of microcontroller which can perform complex tasks and therefore improve usability. Now that entire systems can be fitted onto single pieces of silicon which include internet capabilities, the IoT field has exploded with a current estimated number of internet-enabled devices to be in excess of 22 billion globally.

As IoT technology improved, so did machine learning and it was not long before developers put two-and-two together to realise that the IoT industry could provide unimaginable quantities of data for use training AI. This eventually led to AI being integrated into products and services such as Amazon AWS and Google Assistant which can learn from their users and improve not only their performance but their capabilities as well. Now that AI and machine learning are commonly being integrated into everyday products industries (such as those involved with manufacture) are beginning to integrate their own IoT sensors into equipment known as the Industrial Internet Of Things. The idea behind this is that machinery and equipment can be actively monitored for failing parts as well as general performance and by sending all the data to a central cloud an AI system can learn from the sensory data to predict issues before they occur.

But using a central cloud whereby all data is sent to does have its flaws. The first is that potentially sensitive data local to the device is sent to the cloud which can be accessible to hackers. The second is that not all networks are reliable and any network outage would result in the local device not having any capability to send data or receive data (including warnings). The use of a single network also makes it problematic for an entire connected process that may rely on all parts working as a network outage would result in the whole process ceasing.

One solution that has started to garner interest is edge computing whereby the local device not only collects data but also acts upon it and runs its own machine learning algorithms. Instead of sending all data and relying on the central server providing feedback the device is also able to analyse its own data to make determinations. Shifting the AI process to local devices also helps to free up network traffic which may be potentially slowed down if all devices on the network constantly require AI processing from a central server. The use of edge computing also allows local devices to keep critical sensitive data to itself with no need for a central server to process and store.

 

Emerson Product Release

Emerson have announced their latest product, the AMS Asset Monitor, which is aimed at the IIoT to not only collect data, but also process it. One of the biggest issues with industrial equipment such as pumps and fans is that they are typically checked anywhere between 30 to 60 days and such a length of time can be problematic as there is an increased chance that the piece of equipment in question can fail. Using IoT devices to monitor vibration, temperature, and pressure can be helpful but as stated previously using a central server is not always the best option. This is where the AMS Asset Monitor comes in.

The AMS Asset Monitor is an edge computing device that not only monitors equipment but also performs predictive and protection tasks. The device houses up to 12 CHARMS (small sensory modules) which are used to obtain sensory data and this data is then processed locally to determine asset health. Depending on the determined health of the asset the AMS Asset Monitor can send an alert to the Emersons Plant Web Options as well as to a user’s mobile device or desktop so that engineers are always made aware of potential problems before they occur.

The AMS Asset Monitor includes an A/D resolution of 24-bit with a dynamic range of greater than 100dB, an amplitude accuracy deviation of less than 1%, useable in both SI and Imperial units, linear scale, and internet connectivity via both 1Gbps Ethernet and Wi-Fi. The unit can be daisy chained with up to 8 other AMS Asset Monitors via a 100Mbps ethernet port in scenarios where an asset requires more than 12 sensory modules. To aid in setup and installation the AMS Asset Monitor includes an intuitive web-browser based interface as well as being pre-programmed with pre-set alert limits.

 

Conclusion

The Emerson AMS Asset Monitor is potentially a game changer for remote industrial health monitoring where the failure of key equipment can be disastrous. By using edge computing to monitor device health, local devices can become less dependent on a central server and network thereby reducing the load on the network as well as removing the dependency on a 100% uptime system.