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5 levels of IOT intelligence

There is a seismic shift under way in the engineering industries. The decreased cost of sensors, the increased amount of instrumentation on assets and need for new revenue streams are forcing engineering firms to re-imagine business models. The fusion of “atoms with bytes” promises to unlock new value previously unrecognised which generate additional revenue streams predicated on intelligence generated from the data. As machines increasingly become nodes in a vast array of industrial network, value is shifting towards the intelligence which controls machines. Intelligent Platformization of machines has begunKeeping in mind this fundamental shift in value from atoms to intelligence, Flutura has defined 5 levels of maturity to assess the machine intelligence quotient of an engineering organisation. The highest level of maturity is "Facebook of machines" with ubiquitous sensor connectivity and the lowest is an asset which is "unplugged" where the device is offline. As organisationsembark on a journey to intensify the intelligence layer in their IOT offering it makes sense to map where they are in their current state of maturity.The 5 levels of machine intelligence with specific illustrative examples are outlined below

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organisation. A vast majority of engineering firms manufacture assets which fall into this category. For example a vast variety of industrial pumps still are completely mechanical devices with no sensors to instrument them.

This is the lowest level in the maturity in the maturity map. At this level of maturity, the device or sensor is 'unplugged' from the network. There are no “eyes” to see the state of the machines at any point in time.The machine is offline to engineering

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This is the next level of machine intelligence which exists in the maturity curve. At this level of intelligence the device is connected to the network. There is also rudimentary intelligence exists on the device to take corrective healing action. Examples of assets having edge intelligence include cars which can alert the drivers to basic conditions which need intervention. Other examples include a boiler which has edge intelligence to switch on/switch off valves based on steam pressure

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At this stage the device can be remotely monitored and monitored from a central command centre network. For example Fluturawas working with an asset service provider who was monitoring the health of connected buildings geographically dispersed and monitored in real time. This requires the ability of the platform to ingest billions of events from boilers, chillers, alarms etc. in real time and make sense of which assets need intervention from the command centre and which assets are healthy.

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This is taking the intimate understanding of assets to the next level. This involves triangulating patterns from historical asset data, its ambient conditions etc. to predict failures, defects etc. At this stage, there is enough causal knowledge available to model when the device would break down and proactively trigger an intervention be it a field visit or a part replacement.

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This is the most evolved state of engineering intelligence where all assets the organisation has deployed is connected in real time seamlessly to field force, head office engineers and command centre observers in real time. Very few of global engineering firms are at this level of maturity.

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As business models evolve driven by pervasive hyper connectivity of devices across industries like

Utility, energy, Oil n Gas, Intelligent building management systems etc,

competitive advantage will shift towards differentiated value adding

intelligence platforms. Fluturaintends to leverage its Cerebra

Signal Studio Platform to accelerate signal detection and

deliver value added business outcomes.

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