Asset Management / Optimization Stands to Take Significant Leaps of Value with the Internet of Things

Last week I talked about the “smart plant”, one of the key areas that is changing and opportunity for a step level of output value is in the “Asset Reliability” / “Operational Continuity”.

The real opportunity in is increasing capacity of through :

• Increased flexibility in the existing assets to run more products, and we understand asset condition through pattern recognition
•  Improved preventive, and “awareness” of asset condition and capability of performing at optimum. The devices / assets are “self aware” and self learning on improvement and conditions so early detection of conditions are seen and corrected in a timely manner.
•  Improved planning and asset utilization through transparency across assets on a site and across sites,

There is a lot of talk around the internet of things (IoT) in the general world but in the industrial world there is huge opportunity just due to the significant number of devices.
Industry pundits predict that by 2020 over 50 billion everyday objects will be connected to the Internet. This does not even include the Industrial IoT and the entire M2M environment, much of which is already in place in our factories, plants, and infrastructure.The initial trend will be to establish one-way communication, ultimately migrating to "many-to-many" communications as more physical objects be-come connected. Connecting all the assets and devices in communities of active tuning, decisions and optimization, requiring a significant rethink and change to current operational management/ supervisory systems and information systems to take advantage, but it aligns with the workforce operational transformation.So if we look at the clear steps that can happen in Asset Efficiency:

1/ Increased information, data in a one way capture of asset information.
This step is the first one and is well under way where increased intelligent devices are monitoring / calculating their performance and the information is logged to an historian. As stated in past blogs we seeing the I/O count between control systems and historians increasing by over a factor of 10. (example a pump use to be 5 to 10 variables, now is 120 to 200, a well head was planned to 50 points now logging 690 points).
Once you have this data companies like Pattern Discovery Technologies (http://www.patterndiscovery.com/) produce solutions that used defined events to investigate through Big Data Techniques asset condition patterns, from this vast historical data, so that better prediction is possible of conditions earlier.

2/ Is by direction, and communities of devices “learning” together and tuning their performance.

So instead of a device/ asset just learning on it’s own, imagine a community or similar type devices learning and sharing their learnings between them. This is not a linear learning of optimization but an exponential learning. So the conditions for a type can be immediately picked up and used by a new device / asset of the same type. Machine learning and community “hub” learning is a powerful predictive capability coming into the market.

Companies like MTELL (http://www.mtell.com/) have introduced some powerful “Machine Learning” capabilities, that combine with their “Transfer Learning” capability. Key is this does not have to wait to new devices/ assets on the plant it can be applied to existing assets, and the “learning” will begin.

The concept of going to “Smart Machines/ assets” that are:

• Self-Aware”
•  Self and Tribal Learning, so improving in predictive understanding of behavior

  Notification and increased analysis capability through powerful tools for asset analysis from the dramatic increase in data available.Now that devices can be connected through wireless to internet, and therefore a “cloud historian that is managed” and these analysis tools can executed centrally, or devices discover each other and learn together provides the breakthrough in the Asset world from predictive to prescriptive.

The Next Decade will see a Dramatic Increase in Asset Intelligence and Asset "Self Capability"

For years, we have seen the evolution of asset management, but as we push the agile industrial evolution, the role of a "self aware" asset is key. The need to have stable high operational continuity is key, and this means assets running at performance and reliability while also being able to absorb change.

 There are 3 significant vectors aligning on the evolution of the asset:

a/ Assets are becoming connected to Internet and network, the cost of putting a wireless device, or 3/4G connection on a device is dropping to a point that it is a no brainier for asset agility, and sustainability.

b/ Asset interaction with other "things" around it, e.g.| Other assets, people and systems, being aware of their condition and state, adjusting it' s own operation relative to their state and expectation so the whole of system runs at maximize efficiency.

c/ The dramatically increasing smart/ intelligence in an asset, and in the next decade we going to see this asset develop of “self awareness" becoming more effective.

An example of this was a coal seam gas well, originally it was expected to be approx. 50 I/O between the device and supervisory system, in the space a year this has grown to 680 I/O remember these are not field I/o many are derived attributes from the control, state and operation of the well. Providing a wealth of information on the status and condition, so risk to operations of that well.

These  assets  are  comprised  of  hardware  (physical components,  instrumentation,  and  communications  hardware).    But software, analytics, and ecosystem play increasingly prominent roles.    The software and analytics provide the intelligence and the ecosystem provides additional support and services for individual assets, fleets of assets, or dynamic networks of assets.   With the addition of software, analytics, and an ecosystem; products (physical assets) may be deployed and managed “as a Service.”

 

 

 

 

The diagram above shows the step changes in what intelligence will mean, most assets today are at the "instrumented and analytic" steps. This is already a dramatic step in an asset being able to determine it's own state, and healthy, and exception handle notifications.

The step change is when assets move to “active" that seek to maintain it's self and bring awareness to maintenance early, and adjust operations it's self to sustain. Then to "goal orientated" seeking to self tuning performance and optimization, so gain the most out of the asset relative to it's surrounding assets that make up the process, and relative to the system and people. 

This approach to assets is a paradigm shift from traditional asset management and brings it down to operational real-time environment, to enable decisions in the NOW on a solid foundation value producing assets.