4. Internet of Things:A game changer for predictive maintenance?

Following my last post on Internet of things , this post aims to look at one way with which  businesses can use IOT to transform a part of their operations.

With the vast number and exponential growth rates of smart, connected devices generating log or status data, businesses are looking for actionable, scalable, and accurate strategies to ensure solution reliability, impact top line revenue, and decrease operational costs.

Predictive maintenance is a way to determine the condition of an equipment still in service, to determine when maintenance should be performed, this approach promises cost savings compared to preventative maintenance.

Predictive maintenance analytics offers the promise of capturing crucial, and often hidden, data in real-time, which when combined with existing data from visual inspections promises to revolutionize the industry, boosting asset availability and service levels.It can be used in various industries such as Airline, Railway, Construction, Manufacturing etc

A good example of  predictive maintenance levaraging IOT can be seen in the airline industry, where GE Aviation is one company embracing the IoT across its production and maintenance activities. Maintenance of aircraft or fleet generally poses a challenge to Airlines, because it can take days to diagnose issues using current methods, however with IoT’s predictive capabilities, airlines can deploy predictive maintenance to improve the efficiency of their Maintenance, Repair and Overhaul (MRO) processes i.e. by integrating sensors into the airport infrastructure, these can be used to flag any likely issues on time, thereby giving them time to act, also to improve the maintenance scheduling and inventory management. This predictive capability could also be used to monitor pattern of fuel consumption which can ultimately lead to fuel usage optimisation and savings in fuel costs. Engine manufacturers also stand to benefit from this if the aircraft engines made are fitted with sensors that can store data on engine performance to improve maintenance.

Some fascinating use cases have emerged recently in the railway industry, a case in point is a company called Deutsche Bahn using smart sensor technology created by a start up called KONUX to avoid infrastructure failure which could lead to disruption to services eg A bridge collapse in UK resulted in all direct services between London St Pancras and all stations north of Leicester, including the major cities of Sheffield and Nottingham to be put on hold. Another use case is that of a company called Sharper Shape have been using drones to map utility networks.  They use machine learning to identify trees that are at risk of falling onto power lines, to prevent disruption to utilities.

Predictive maintenance using IoT still has a ways to go, but it does look like more and more organizations are starting to realize real value from their investments. In part three of this blog I will be writing about Internet of Things and the ways it affects consumers.

 

REFERENCES

 

Deglmann, J. (2017). GE Aviation Outlines CMC Facilities Progress. [online] MRO Network. Available at: http://www.mro-network.com/advanced-materials-composites/ge-aviation-outlines-cmc-facilities-progress [Accessed 10 Mar. 2017].

http://railwayinnovation.com. (2017). How is Deutsche Bahn using smart sensor technology to avoid infrastructure failure?. [online] Available at: http://railwayinnovation.com/wp-content/uploads/2016/08/How-is-Deutsche-Bahn-using-smart-sensor-technology-to-avoid-infrastructure-failure.pdf [Accessed 10 Mar. 2017].

http://sharpershape.com/

https://www.konux.com/case-studies/

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