Internet of Things (IOT) and Machine learning are new technology trends that are booming individually: we will look at how to combine these concepts and technologies by layering machine learning on top of IOT data and driving significant insights for clients via specific use cases like predictive maintenance. Let’s look at some state of the art use cases and subsequent benefits delivered in this space to dig deeper into the “art of the possible”.
12. Premium Car Brand
Predictive Maintenance on camshaft drive
chain failures
Realized cost
savings through
predictive
maintenance
Increased
Customer
Satisfaction
Big Data Platform to ingest, analyze and process massive
car sensor data
Predict extensions of camshaft drive chains and identify
potential root causes
Recommend predictive maintenance action to replace or
adjust camshaft driving chains during routine workshop
Reduce visits to save costs, warranty cases and increase
customer satisfaction
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13. Large Oil & Gas Company
Prediction of equipment failure to reduce
costly, unscheduled downtime
Collecting and Ingesting IOT data from 10,000 wells
across 700 facilities and 500 fields
Predictive and real time event detection for pump failure
in hours, days or weeks based on historic and current
streaming sensor information from ESP lifted wells
Helped the client understand the precursors of failures
and optimize logistical maintenance scheduling around
well maintenance and repair
3 months to value with a 10% reduction in downtime
and 20% reduction in operational costs
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14. Williams Martini Racing
A tire optimization application to mine data
for real time insight
Connected Formula One cars with 1000 different
channels of data coming off a car generating 60-80 GB
worth of data from each car over the whole race
weekend
A tire optimization application to isolate the
performance of its tires from the raw lap time data it
receives during races by the use of proprietary
algorithms
Unlocking the value of existing data in an intuitive,
highly visual interface to help improve car design and
track performance as well as empower Williams’ staff to
make better, data-driven decisions.
Real-time collaboration on data between Williams
staff at headquarters and trackside anywhere in the
world with strategists now spending 10% of their time
manipulating data vs. 70% before
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