Predikto, Inc. Partners With New York Air Brake to Incorporate Predictive Analytics In NYAB’s LEADER Advanced Train Control Technology Solutions

ATLANTAJuly 14, 2015 /PRNewswire/ — Predikto, Inc. today announced a new collaborative effort where New York Air Brake will incorporate Predikto’s auto-dynamic predictive analytics platform, MAX, into the company’s LEADER advanced train control technology solutions via its internet of things (IoT) initiative.

New York Air Brake, a subsidiary of the Knorr-Bremse Group (Munich, Germany), an innovation leader and supplier in the rail industry since 1890, will integrate a new predictive analytics component to its Advanced Train Control Technology solution, LEADER (Locomotive Engineer Assist/Display & Event Recorder). The mutually developed solution will now leverage a suite of predictive analytics software applications engineered by Predikto, Inc. Predikto’s patent pending solution, called MAX, is an auto-dynamic machine learning engine that draws upon LEADER train data in addition to capturing data external to the train itself, such as weather and line of road conditions. MAX is a self-learning artificial-intelligence solution that adapts itself to rapid changes in context in near real-time in order to provide the most accurate forecasts possible across an array of use-cases.

“Integrating predictive analytics with the rich train information from LEADER will allow the railroads to utilize their data to proactively identify opportunities to improve operating efficiency and rail safety,” said Mario Montag, CEO of Predikto. “Partnering with a premier technology company in the rail industry, such as New York Air Brake, will allow Predikto’s award-winning platform to make a defining impact on the rail industry.”

The predictions provided by MAX will enable new and existing users to incorporate advanced data analytics to enhance the capabilities currently available through LEADER. Predikto’s MAX platform has already proven success within the rail industry through forecasting failures and health in rail equipment ranging from bullet trains in Europe to wayside detection equipment in North America. This partnership will allow for the deployment of dynamic predictive capabilities that include a locomotive energy efficiency forecaster, a braking efficiency forecaster and track health. The LEADER/MAX solution is poised to revolutionize the rail industry by providing advanced insight to improve velocity and operating efficiency.

“You can have data without information, but you cannot have information without data.  Predikto’s MAX allows us to extract every bit of information and turn it into actionable insights that will improve visibility into operations, provide innovative solutions to improve safety, and provide clarity into the critical maintenance and performance indicators that impact the bottom line most,” states Greg Hrebek, Director of Engineering for New York Air Brake.  “The capability offered between us through this collaboration is unprecedented in the rail industry and will rapidly accelerate the value of the investment the railroads have made into locomotive onboard intelligence.”

About New York Air Brake

New York Air Brake, Inc., headquartered in Watertown, NY, has a long-standing history of innovation and technology in the rail industry ranging from providing advanced braking technology for trains to train control systems. New York Air Brake’s mission is to provide superior railroad brake and train control systems, products, and services with high quality and high value. For more information visit the New York Air Brake website at www.NYAB.com.

About Predikto, Inc.

Predikto, Inc., headquartered in Atlanta, GA, provides actionable solutions for the rail industry as well as industrial equipment and fleets using predictive analytics. Its proprietary data analysis and prediction engine is built on an auto-dynamic machine learning protocol that adapts to changing environments in near real time.  Predikto specializes in operationalizing predictions of key industrial events like asset failures and poor asset health to enhance a company’s overall performance.

The company is comprised of engineers, developers, academics, and industry professionals. Predikto’s technology solution enables companies to achieve seamless operational functionality, efficiency and exponential return on their asset investment.

For more information, visit www.Predikto.com.

 

SOURCE Predikto, Inc.

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Maintenance Triage – Uptime Magazine

Predikto has been featured in the article “Maintenance Triage: Identifying Sick and Injured Assets to Improve Population Health” in the Uptime Magazine February 2015 issue. The author of the article, Will McGinnis, is a Mechanical Engineer from Auburn University working at Predikto as a Senior Software Architect. At Predikto, he uses advanced machine learning techniques to leverage pre-existing data in asset intensive industries to predict failures, identify bad actors, and impact bottom lines.

The mission of Uptime Magazine is to make maintenance reliability professionals and asset managers safer and more successful by providing case studies, tutorials, practical tips, news, book reviews, and interactive content.

To view the article, click on the image below

Predictive Analytics in Uptime Magazine

Using Predictive Analytics in Maintenance

predictive analytics for maintenence
Predictive Maintenance is the best type of maintenance a company can undertake, but not all assets classes and applications justify Predictive Maintenance. That is why the best type of maintenance is the type that works for each client. The latest technology in Predictive Maintenance is the use of Predictive Analytics. In some cases Predictive Analytics is reaching accuracies above 95% to predict an asset failure. These results are much higher when compared to traditional predictive maintenance techniques like Lubrication Analysis, Infrared, and Vibration. These are all excellent techniques and companies should continue using them if they are seeing success reducing downtimes, extending the lifetime of equipment, and subsequently saving money.

In the MAPCITE blog, Eric Spiegel, CEO of Siemens U.S.A., consider that “while analytics were implemented widely in industries such as banking and communications initially, we view capital-goods organizations as a huge untapped opportunity, driven primarily by the “Internet of things” and the significant potential to optimize product development, supply chain and asset related services. One example is predictive maintenance – if we were able to better predict when critical and expensive equipment is most likely to fail, we could reduce downtimes, extend the lifetime of the equipment, and realize significant savings”. Read the entire story HERE.

Here comes the flood-analytics, Manufacturing Industries!

Manufacturing Industries

Yes – we have heard about the magic of predictive analytics and how it has helped various companies and industries in predicting the rise and fall of companies’ finances, social media and marketing and such but could the same predictive analytics methods aid manufacturing industries today?

According to Bala Deshpande’s article, manufacturing industries are not entirely oblivious to the idea of collecting data. As a matter of fact, you could call them ‘The Forefathers of Data Collecting’. Manufacturing industries have been collecting data for years on the company’s current operations and quality of their products. However, the time has come for manufacturing industries to start digging these data sets a bit deeper to improve their operations so much so that, companies would be able to improve notably their production yield.

The benefits of manufacturing industries engaging in predictive analytics can be seen when the production process becomes even more efficient and cuts unnecessary costs (i.e. unexpected machine failure). Deshpande highlighted a small company in the manufacturing industry that has already started to engage in predictive analytics by installing overhead GPS sensors that notes down the number of workers working on a particular project and if that project requires assembly so to calculate how extensive the machine is being used and predict any machine failures and such.

Whether manufacturing companies like it or not, engaging in analytics is inevitable. Especially if competitive manufacturing companies are using the same predictive analytics to measure how likely they are going to be performing much better than the other companies!

Predicting Production Delays and Expected Yields in Future Batches

Predikto will be releasing a new Advanced Analytics solution that will enable operators at continuous manufacturing facilities to predict production delays, asset failures, and expected production yield for future batches without the need to send us sensor data on a daily basis.

Operators will enter future batch settings like Steel Quality, Billets, Speed, Temperature, and a handful of other information.  Predikto will show in a simple graphical web form the risks embedded in the batch and the recommended settings to maximize yield and reduce the probability of having delays or failures with their equipment.  This could be a game changer. Stay tuned…

steelbarssm

China Pollutes Less and Impacts Global Iron Ore Pricing


Iron Ore Pellet 2014

Some sources estimate that China has over 400 Steel Mills. Therefore, a shift to reduce pollution has lowered output from some “bad actor” mills in China reducing overall supply while the global demand for steel is expected to increase in 2014. As basic economics suggests, increase demand with lowered supply means higher prices. The Platts outlook report on global iron ore stated that pellet premiums are expected to remain firm into the first half of 2014 as supply growth struggles to keep pace with an increase in global demand.

Steel Mills are very susceptible to global demands and pricing fluctuations. Stay tuned for a new Advanced Analytics solution by Predikto to help steel mills predict asset delays while also predicting production yields by batch. Plant Managers and Maintenance Managers are constantly striving to maximize yield, reduce asset failures, and improve overall efficiencies. We believe Predikto is unique positioned to help Steel Mills make significant improvements to their bottom line.

Reactive Maintenance and the Telephone Booth

I was walking through the streets of New York recently and ran into a public telephone booth. I was amazed at my reaction since phone booths are almost extinct. Earlier that day my family went to the Museum of Natural History to see dinasours up close and personal, and it’s amazing how all these different and interconnected animals are extinct as well. It got me thinking as to what practices in industrial manufacturing would become extinct in the next decade or two.

The fact that 50% of the experienced manufacturing workforce will be retiring in the next 5 to 10 years is adding a lot of pressure to organizations to improve automation and depend less on these seasoned “experts”.

So if improvements in technology and automation are key to ensuring US manufacturing continues to stay ahead of the pack, then what areas will become extinct? I believe organizations focused on reactive maintenance will eventually become extinct. These reactive firms who have not implemented best practices around preventative or predictive maintenance will be gobbled up by their better performing competitors, will be acquired by PE firms who will then trim the excess fat, or simply go out of business.

What do you think? What areas of manufacturing or maintenance do you think will become extinct in the next decade or two?