Amazon bets on condition monitoring

Late in 2020, Amazon announced the launch of Monitron, an end-to-end machine monitoring system powered by its AWS infrastructure. Blake Griffin, Market Analyst at research specialist, Interact Analysis, believes this move demonstrates the increased importance that condition monitoring will play in the future.

On December 1st, 2020, Amazon announced a suite of new AWS machine learning services. To many, this announcement appeared to be Amazon’s launching off point towards being a major supplier of predictive maintenance solutions. However, this announcement follows a long history of Amazon carving out its capabilities in industrial digitalisation. Since its 2018 release of AWS IoT Sitewise – a service that enables its users to gather and organise asset-health related data housed in repositories such as a historian – Amazon has consistently added to its industrial digitalisation offering. Now, the company has a highly competitive solution with one capability unique to Amazon.

In some ways, Amazon’s announcement of its new suite of machine learning services represents a rounding out of a predictive maintenance offering rather than a jumping off point. When manufacturers are looking at implementing predictive maintenance into their facilities, they ask these fundamental questions: Which assets do I have visibility into already, and how can I leverage this data? Which assets do I not have visibility into, and what can I do to change that?

The announcement of AWS IoT Sitewise was Amazon’s solution to the first question. Many manufacturers in process industries generate large amounts of data from the devices controlling their machines. AWS IoT Sitewise was developed so manufacturers could more effectively utilise this data for condition monitoring/predictive maintenance purposes.

Strategically, this was a logical first move. Amazon already had a wealth of analytical tools it could deploy to make use of data housed in a historian; the only thing needed was a mechanism for gathering and organising that data to be analysed.

Fast forward to Amazon’s recent announcement, and we see the company moving to provide a solution to question two. Assets that are often cited as being ‘offline’ from a condition data perspective are the mechanical portions of a motor-driven system, i.e. induction motors, gearboxes, bearings blocks, etc. These components are numerous throughout factory floors, and their failure can represent a significant loss of production. The industry has responded to this need by offering smart sensors, a wireless-enabled sensor that can be connected to the side of a motor to gathering data on vibration and temperature behaviour. When combined with machine learning algorithms, these two data points can quickly illuminate what kind of stress motor components are facing and alert its users to problems ahead of failure.

One of the services announced in late 2020 has been coined Amazon Monitron. The solution utilises smart sensors and gateways produced by Amazon to offer up data on the health of motor system equipment, effectively solving the problem of gathering data on assets not being monitored via historian data. This solution is in direct competition with more familiar predictive maintenance providers. In our view, the announcement of Monitron means Amazon now has a solution that fully addresses the needs of manufacturers looking to invest in predictive maintenance as part of a broader industrial digitalisation initiative.

Every platform offered by the major providers of predictive maintenance is built on cloud storage technology offered largely by either AWS or Microsoft Azure. The full development of Amazon’s industrial digitalisation offering represents the first time a supplier can provide both the cloud storage and analytic capabilities under one entity.

It is difficult to foresee what impact this will have on the partnerships AWS has in place with current industrial digitalisation providers. However, what is easy to see are the numerous advantages Amazon will have in potentially winning the business of those investing in industrial digitalisation for the first time. If customers are looking to utilise the cloud for their industrial digitalisation initiatives, Amazon will represent the fewest number of touchpoints between customer and supplier during the sales process. Additionally, many manufacturers may already be using AWS for cloud storage but have yet to invest in further industrial digitalisation technology. In these scenarios, Amazon would already have a foot in the door, which would yield them an advantage when the time comes for users to begin evaluating providers of digitalisation.

At the very least, this announcement should be taken as a signpost of future growth within an already fast-growing predictive maintenance market. Amazon does not enter markets that are expected to appreciate modestly; it enters markets whose opportunity could one day be worth billions of dollars.

www.interactanalysis.com