Tag

predictive analytics

High school student predicts seizures using Falkonry LRS

By | Industry Presentations

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

By | Industry Presentations

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.

Repost: Deloitte University Press: Industrialized Analytics

By | IT/OT Management

This post from Deloitte University, first published in February of 2016, discusses ‘data as the new oil.’ It’s a great article that underscores the significance that data can contribute to an organization’s agility and ultimately, competitive position in the market.  It also, as they state, is the foundation to the digital transformation that industry is now undergoing.

Think back just a few years if you are in manufacturing.  The #cloud, #Internet of Things, even #virtualization were far-off concepts that had little to no relevance in your world. Your goals–keeping your operations running efficiently, avoiding downtime and unexpected conditions–were paramount to your GMP (good manufacturing practices) and digitization mostly meant taking real-time process data from a DCS (Distributed Control System) or PLC (Programmable Logic Controller) and using it with a data historian for process control.  The influx of applications such as enterprise manufacturing intelligence, dashboards, and other ‘big data’ systems brought a step change to the way you understood your operations, and the cause/effect of events.   Being able to now isolate, or predict, events was huge, and data (and the effective use of it) was the reason for this efficiency.

Now, another step change is occurring. Advanced techniques such as Artificial Intelligence, Machine Learning and Pattern Recognition offer insights into behavior of the assets, people and processes.  Data becomes not only oil, it’s gold.  It’s the key to your understanding and efficiency.

Falkonry is one of those applications riding the data wave; a window into your process behavior using your resident subject matter experts, along with the data, to provide context and relevancy to that data.   See more about Falkonry pattern recognition here.

 

 

Falkonry Service Improves the AI Experience: More Connections, Better Deployment Options

By | IT/OT Management

Falkonry Service was introduced a few months ago to improve the Falkonry solution fit and flexibility. As we’ve interacted with customers over the last several months, we’ve gained a greater understanding of the many different types of solutions people are trying to build with embedded pattern recognition capabilities provided by Falkonry. This release is a reaction to those needs and includes:

  • Improved data consumption capabilities
  • Expanded connection options
  • Simpler private deployment options

Falkonry Service Architecture

 

You can watch videos about Falkonry on our Website to learn more.

Improved data consumption capabilities

To better address current needs and in anticipation of future needs, we added a new core architectural element to Falkonry called Event Buffer.  Event Buffers separate the responsibility for managing data inbound to Falkonry from Pipelines that process that data.  One obvious benefit from the addition of Event Buffers is that one data source can supply data to multiple pipelines. This capability can be used to simply allow reuse of previously loaded data or to support more complex real-time simultaneous pattern recognition scenarios. Each pipeline, for example can make independent choices on how to interpret and process the same data stream.  An additional capability associated with Event Buffers is the ability to chain Pipeline executions to each other – i.e. to route output from one Pipeline to an Event Buffer that feeds other Pipelines.

Event Buffers also support and provide a focus for a growing set of capabilities related to data consumption.  The new release, for example, allows users to supply data to Falkonry in ‘Data Historian style’ format – sets of points in the form of a <timestamp, tag, value> triple format.  This augments the tabular structure supported previously.  Likewise, JSON (line delimited) support was added to complement CSV.

Expanded Connection Options

The new release also makes it easier to connect to Falkonry and to embed it in your solutions.  New capabilities include:

  • MQTT connectivity
  • Webhooks connectivity
  • Updated REST API
  • Expanded set of client libraries
  • Updated Splunk plugin

The Falkonry UI makes it easy for an Event Buffer to subscribe to a MQTT topic or for a Pipeline’s outflow  to be published to a MQTT topic or a Webhook URL. Additional subscription and publication options will be added in the future.

Client libraries are now available for Javascript and Python and others (e.g. C# and Java) are under development.

The Falkonry Splunk App that makes it easy to bi-directionally connect Splunk to a Falkonry Service instance was updated and streamlined, and plug-ins for other data platforms are under development.

Simpler Private Deployment Options

While the Falkonry Sandbox provides a useful and effective path for experimentation and early solution development, private deployments are the primary way Falkonry gets delivered.  Private deployments can be either:

  • Virtual Private Cloud (VPC) Deployments: Falkonry deployed in customer specific VPCs on public provider infrastructure like Oracle, Microsoft Azure, Google Compute Engine (GCE), Amazon Web Services (AWS)
  • Private Deployments: Falkonry deployed on customer controlled compute and storage infrastructure.

To make installation much simpler, Falkonry now offers a downloadable option for Private Deployments.

Falkonry AI Augments Operational Decision-making

As extreme growth in the generation of live operational data continues, the need to build solutions that recognize patterns in this data grows in parallel. Falkonry is committed to making AI-based pattern recognition an easy to incorporate component of any operations-oriented solution, and this release furthers us down that path.

For more information, please visit www.falkonry.com

What Does Falkonry’s $5.3M in Equity Funding Mean for Industry? What do Zetta Venture Partners and Polaris Partners see in Falkonry??

By | Uncategorized

Falkonry discovers, recognizes, and predicts operating and performance conditions from time series data, using machine learning/pattern recognition. From its inception, Falkonry has had a single purpose: to make industrial operations  more productive. Falkonry achieves this by applying  advanced signal processing techniques to:

  • Gain yield/OEE improvements not possible without advanced pattern recognition
  • Create a whole new ‘view’ of process and asset operations through patterns in operational data
  • Put advanced pattern recognition technology into the hands of process and industrial engineers — those who know and understand the operation of their assets and processes.

Investors Zetta and Polaris recognize the ground-breaking shift in yield outcomes enabled by Falkonry’s advanced pattern recognition technology. Their backing, and the backing of Falkonry’s partners and customers, signal the transition of Falkonry’s innovations from a nascent technology to a truly industry-impacting software offering.

 

pattern recognition

Falkonry Pattern Recognition

 

Falkonry advanced pattern recognition technology delivers an intuitive UI designed for process and industrial engineers — the company domain experts; no data engineering or scientists are needed. It allows companies that generate massive amounts of data, such as those in the process industries (oil and gas; metals, mining and minerals; pulp and paper) to detect, recognize, and monitor patterns of operation that are only “visible” across complex, multi-variable, time-series data. Recognizing patterns of asset and process operation illuminates areas across  assets and processes that for OEE and yield improvement; where bottlenecks and choke points reside; and where in the process flow tuning can be applied.  Falkonry’s customers report gains in process visibility; uptime; and process throughput.

For more information on Falkonry, please visit www.falkonry.com.