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Industry Presentations

High school student predicts seizures using Falkonry LRS

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At Oracle OpenWorld 2017, Falkonry CEO, Nikunj Mehta and Cupertino High School student, Ayush Jain, took the stage at the Innovation Studio to show how easily Falkonry LRS machine learning system can be used to find invisible patterns in data.

While Falkonry is typically used to solve hard operational problems in industrial environments like semiconductor fabs, automotive plants and chemical manufacturing, it can analyze any multivariate time series data and discover the invisible patterns that can provide reliable, early warnings of problems.

In this example, Ayush studied EEG data using Falkonry LRS automated machine learning to see if an early warning to an epileptic seizure could be detected. While Ayush was new to EEG data and to Falkonry, he had a very ambitious goal. Within 2 weeks, he had to create the prediction system for seizures. See the results from Ayush’s project in the video below.

This is a powerful example of how predictive analytics and automated machine learning can be used for positive change. It also shows the simplicity and ease-of-use of the Falkonry LRS system. Great work Ayush! We know you will go on to solving more hard problems and leave your mark on society, and are really glad to be a part of your journey.

If you’re interested in learning more, check out our Product page to watch a short demo of Falkonry LRS ready-to-use machine learning system.

OSIsoft Intern Creates System to Predict Energy Pricing

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OSIsoft customer support intern, Miwa Teranishi, leverages the power of OSIsoft PI System and Falkonry LRS ready-to-use machine learning system to create an energy price prediction system to understand electricity pricing in Kyushu, Japan. With her system, energy consumers could identify the best time to consume or avoid electricity use during a day. Let’s take a look at her project in this video:

As Miwa highlighted, Falkonry LRS “makes creating models quite simple with no programming and no knowledge of data science algorithms required.” Further, the integration between OSIsoft and Falkonry allows customers to quickly unlock value from existing data assets and deliver insights from their PI System.

Thanks Miwa for a very interesting use of PI System and Falkonry LRS. This is a creative example of how automated machine learning could be applied to everyday challenges, like the daily, fluctuating price of electricity.

In addition to PI System, Falkonry LRS machine learning is easily integrated into many popular solution platforms such as Splunk and Microsoft Azure IoT, accelerating the time to value for customers.

If you’re interested in learning more, check out our Product page to watch a short demo of Falkonry LRS ready-to-use machine learning system.

Ciner creates early-warning system for mining operations with Falkonry LRS

By | Industry Presentations

At the OSIsoft User Conference in San Francisco, joint customer, Ciner Resources (pronounced “jin-ner”) spoke about the digital transformation they were experiencing by employing OSIsoft’s PI system along with Falkonry’s condition recognition software.

The three Ciner speakers included their CIO, SMART plant lead and their process engineer.  They spoke about the challenges, triumphs and future goals of digitally transforming their business.  Ciner’s mine and processing plant in Wyoming has a ball mill, called a vertimill, that was experiencing unexpected downtime due to fluctuations in ore grade. Each time the vertimill clogged due to “unknown” low quality ore grade, it cost the company $30,000/hour of production potentially leading to a loss of $720,000 per day.

Ciner’s engineers and practitioners worked with OSIsoft and Falkonry to look into historical data to devise a solution. Together they employed the PI System and Falkonry’s machine learning software and within a few weeks, were able to recognize the patterns that led to blockage conditions. From there, an early-warning system was created to detect low-quality ore grade upstream of the vertimill in order to mitigate downtime events. Where the raw material took two hours from intake to reach the vertimill, Falkonry discovered 10 hour early warnings which surprised the process engineers.

In the video below, Wyatt Keller, Process Engineer at Ciner, shares his experience using OSIsoft and Falkonry LRS ready-to-use machine learning system.

If you’re interested in learning more about our success with Ciner, check out our customer video page to watch additional videos.

SparkLabs Demo Day Features Falkonry

By | Industry Presentations

SparkLabs Demo Day was a terrific experience for Falkonry and we received a great reception in Seoul. The environment and the stage was electrifying as there was a lot of energy on display. It was a privilege to present our technology solutions to an audience of thousands, including fellow entrepreneurs plus investors, corporate executives, media and other individuals all interested in the latest technology trends and innovation.

Here’s a shot of me in front of the audience, before starting my presentation. 

I spoke about why industrial IoT matters and why industrial predictive analytics is fast becoming a $100B market. I showed a powerful example from our customer Ciner, who now is able to avoid costly downtime with their vertical mill and save $30K/hour. In fact, we recently posted a video of their CIO, on the Customer Videos section of our Website, explaining how Falkonry was quickly able to help his company implement predictive maintenance, process optimization and achieve sizeable cost savings.

Next I spoke about how our technology can easily find “invisible” warning signs for early intervention, and how this translates to a “Spiral-Up” effect. What our customers have shown is that once they start to find these invisible warnings, their confidence in self-service predictive analytics grows and they want Falkonry to watch more IIoT patterns, which leads to finding deeper 2nd and 3rd level conditions of importance. This often excites the team of practitioners and they organize to leverage our software to get even better predictions. Downtime goes down, revenue increases, and the spiral-up continues…

I finished our pitch by showing how we differentiate ourselves in the marketplace by being a horizontal technology suitable for every industry and is optimized for use by practitioners. There is no need to hire a team of data scientists since the data science is built into our software. We’re multi-system, multi-industry and multi-market.

I want to especially thank my two SparkLabs partners for helping Falkonry get the most from this event. First, Jay McCarthy, who was my pitch partner was a terrific guy to work with and helped me polish my message. Second, Alex Namkung, who has directed the IoT cohort was phenomenal in lining up so many valuable conversations. I cannot forget the booth help from the wonderful Kevin Choi, who ensured that everyone who came to our booth had an opportunity to hear the Falkonry story.  You can view highlights from my presentation here. Enjoy!

As usual, we appreciate your feedback and sharing with your colleagues!