Airplane, Heal Thyself: Introducing the Asset Efficiency Testbed

Airplane Heal Thyself Introducing the Asset Efficiency Testbed

On a recent trip home from a business meeting in Washington DC, I experienced the (all-too-frequent) frustration of having my flight delayed. After boarding the flight and sitting at the gate for almost an hour, the pilot announced that we would be grounded for at least two more hours. It was discovered – while preparing for take-off – that a key part in the aircraft’s landing gear was malfunctioning and its replacement needed to be shipped in.

As I sat at the gate with my fellow wayward passengers, I couldn’t help but think about how predictive maintenance could have played a key role in preventing this situation.

In fact, an aircraft landing gear predictive maintenance use case is the first demonstration of the Asset Efficiency Testbed, the latest Internet of Things innovation from the Industrial Internet Consortium. Infosys is leading the development of the Asset Efficiency testbed, with support from international partners Bosch, Intel and PTC.*

OK, I realize “asset efficiency” might sound like something a mutual fund salesman would sell you.  However in this case, it is an exciting technology opportunity that promises to save significant downtime, dollars and even lives. This is big news for high tech manufacturers in particular, and for just about any enterprise that cares about maximizing the performance of its critical infrastructure assets.

Asset efficiency: the elephant in the room

Asset efficiency isn’t a revolutionary new concept, but it is highly underleveraged.

With equipment and system processes becoming intelligent, virtually every activity in the industrial enterprise – say an aircraft, factory or oil field – generates data. If that data is monitored and turned into meaningful insights, it gives maintenance engineers the opportunity to accurately anticipate and correct failures. That’s a powerful opportunity to maximize performance, conserve energy, reduce waste, improve quality and grow profits. And yes, sometimes even save lives.

However, this kind of smart management isn’t as universal in the industrial enterprise as you might expect. A recent study by Infosys and the Institute for Industrial Management (FIR) at Aachen University revealed that 85 percent of industrial and process manufacturing companies in China, France, Germany, the UK, and the United States are aware of the need for driving asset efficiency, but only 15 percent have implemented machine data technologies to do this.

So why are manufacturers ignoring the asset efficiency opportunity? The study found key challenges include lack of instrumentation of assets, missing real-time data analytics, missing information from related systems and lack of focus on other aspects of efficiency like energy, utilization, operations and serviceability.

Mind the gap

The Asset Efficiency testbed is designed to fill this gap, using a holistic approach incorporating operational, energy, maintenance, service and informational considerations (see graphic). Features include:

  • Condition Monitoring:  Measures and tracks key health parameters of the assets and ensures they lie within allowed ranges.
  • Diagnostic Analysis: A powerful analytics engine analyzes data, compares with past and related data in context and identify anomalies.
  • Prognostics: Algorithms predict remaining useful life for the assets or the components of the asset.

Airplane Heal Thyself Introducing the Asset Efficiency Testbed figure 1

To learn more about the Asset Efficiency testbed, please visit our website. You can also view a demonstration and meet me at the IOT Solutions Congress in Barcelona from 15th to 17th September 2015.

* Thanks to Jayraj Nair of Infosys for his assistance with this blog, and to the Asset Efficiency testbed collaboration partners:

  • Infosys is leading the effort, designing the solution, developing the application, adapting advanced engineering knowledge for the use cases, and supplying the Infosys Information Platform (IIP) as the analytics engine.
  • Bosch supplies the sensor technology, acquiring data from the edge, providing device management and scalability by the Bosch IoT Suite and ProSyst IoT middleware mPRM and mBS. The Vorto code generator enables M2M modelling.
  • PTC supplies the Thingworx Application Enablement Platform (AEP), used for creating dashboards, widgets and other user interface elements.
  • Intel provides the Moon Island Gateway used for data aggregation at the edge, as well as horizontal infrastructure in collaboration with HP.

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