Greg Adams was a recent guest blogger on the ARC Advisory Group’s IIoT newsletter. We see a lot of data and it is interesting how mundane and often overlooked data can contain meaning. Read how counting toilet flushes is helping to increase the uptime and reliability of bullet trains. http://industrial-iot.com/2015/10/how-wc-flushes-relate-to-locomotive-reliability/
Predikto, a leader in Predictive Analytics solutions Transportation, has begun to deploy their machine learning / artificial intelligence software to help improve equipment reliability at global companies.
Click on the article to read about actual use cases and gain an understanding of this disruptive technology.
ATLANTA, July 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.
The Internet of Things is generally defined as “Smart” + “Connected” + “Edge Devices” (Planes, Trains, Automobiles, Industrial & Farming Equipment, Medical Equipment, and Consumer Electronics)
Predikto focuses on putting the “smart” into managing smart connected devices, equipment and complex capital assets in order to forecast asset behavior/performance.
Industrial asset OEMs, operators and maintenance organizations are challenged by equipment performance degradation and failure as they impact uptime and efficiency. While reliability and condition-based solutions have been around for many years, predictive analytics (machine learning) is providing significant new capabilities to improve performance and profitability.
Approximately 2,000 hardware, software and business leaders attended the second annual O’Reilly Solid 2.0 IoT conference in San Francisco. Attendees were given the opportunity to vote on the startup they believed was making the most innovative impact in the field of industrial or consumer IoT. Of the 30 or so startups at the conference, Predikto was voted best startup by attendees for its telematics / IoT based predictive analytics, predictive maintenance and asset health management solutions.
This was great exposure for us at Predikto, and now we are up for 2 awards at the upcoming Solutions 2.0 Conference in early August. We are going head to head against some big players in the industry in the categories of Asset Condition Management and Asset Management. Mario Montag, Predikto CEO, will be presenting on the topic of Predictive Analytics in Asset Management. This is another indication of the high demand for IoT products and solutions, the acceleration of Predikto within the Industrial Internet market and the large innovative technology community in Atlanta.
Mario Montag was quoted after the Solid Conference: “It is great to see validation from the market and conferences with regards to our Solution based predictive analytics technology and approach. We are not a tool to enable customers to do more. We deliver results and bring to light full transparency on the ROI and impact we are having to solve real problems with asset reliability.”
We have also been getting some great traction with customers and partners. We recently announced a partnership with New York Air Brake, subsidiary of the Knorr-Bremse Group in Germany, to 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. See the full story here.
Needless to say we are all very are all very excited about the awards and recognition Predikto is receiving and it is legitimizing the need for a real solution in predictive analytics for the IIoT.
I’m about 4 months into the job here at Predikto as VP, Sales. The predictive analytics market is an exciting new market with predictably (pun intended) its share of hype. Nevertheless, this is key niche of the Industrial Internet of Things sector. I’d like to share some observations on what I’ve learned thus far.
We focus on asset-intensive industries, helping organizations leverage the terabytes of data they have accumulated to anticipate the likelihood of an adverse event, whether that is a battery on a transit bus about to fail, or indications that a fuel injector on a locomotive diesel engine, while still operating, is doing so at a less than desired level of performance. We predict these events in a time horizon that allows the customer to take action to rectify the issue before it creates a problem, in a way that minimizes disruptions to operations. Our technology is cutting edge Open Source, leveraging Spark, Python and Elastic Search hosted by AWS.
The use cases we’re being asked to solve are fascinating and diverse. Some companies are contacting us as part of an initiative to transform their business model from selling capital assets to selling a service, an approach popularized by Rolls Royce with their jet engines, the “power by the hour” approach and similar to the software industry’s transition from selling perpetual licenses with maintenance contracts, to selling Software as a Service (SaaS). In order to sell capital assets like construction equipment and industrial printing equipment this way, our customers will offer service level agreements, with Predikto in place to allow them to proactively deal with issues likely to degrade their service commitment. So while our tactical focus has been on helping clients maximize product “uptime”, the strategic driver is helping them transition to a new way of generating revenue while getting closer to the customers. It’s been gratifying to realize the impactful role our offering is playing in facilitating these transitions.
Other organizations are complex, asset-intensive businesses, where an equipment failure can have a cascading effect on revenues and customer service. For example in the work we are doing with railroads we’ve learned there are a multitude of areas where sub-optimal performance of equipment or outright failure, can have significant impact. The North American railroad network in 2014 set new records for revenue-ton-miles, a key efficiency metric; this was accomplished over a rail network which is highly congested. In this environment, a delay has huge ripple effects. Any number of factors can lead to a delay, ranging from a rockslide blocking a section of track to a locomotive breaking down, to a wheel failure on a rail car, which can cause a derailment. On top of this, in order to operate safely and comply with government regulations, railroads have invested heavily in signaling and equipment monitoring assets, as well as machinery to maintain the track and roadbeds, which must work reliably. Our abilities to implement in weeks and generate actionable predictions regarding locomotive and rail car health, as well as monitoring other equipment and even the condition of the rails, are making a major difference in helping to facilitate efficient, safe rail operations.
Having a blast…more to come.
Kevin Baesler, VP of Sales
Transit provides more than 10 billion passenger trips each year, which represents more trips each month than all of the Nation’s airlines combined will make in a year. When transit assets are not in a state of good repair (SGR), the consequences often include increased safety risks, decreased reliability, higher maintenance costs, and an overall lower quality of service to customers.
The Moving Ahead for Progress in the 21st Century Act, MAP-21, states in its section 5326 that the Federal Transit Administration (FTA) will implement a national transit asset management system, including “a strategic and systematic process of operation, maintaining, and improving public transportation capital assets effectively throughout the life cycle of such assets.”
The Transit Asset Management System will include at minimum:
- The definition of the ‘state of good repair’, that should include standards for measuring the condition of equipment, infrastructure, rolling stock, and facilities of the capital assets of the public transportation systems that receive FTA funding.
- Federal financial assistance to develop a transit asset management plan.
- Reports of the conditions of the systems, and any changes to it.
- An analytical process or decision support tool that allows for the estimation of capital investment needs, and the prioritization of the public transportation systems.
- Technical assistance to the FTA funding recipients.
Predictive Analytics tools for Asset Management can help to meet the requirements of the Transit Asset Management systems, 49 U.S.C. 5326, especially those points related to the implementation of a national transit asset management system, that include an analytical process or decision support tool for use by public transportation systems.
The Moving Ahead for Progress in the 21st Century Act, also known as MAP-21, is a law that includes provisions intended to improve security reducing crashes, injuries and fatalities involving large trucks and buses.
Many of the provisions in MAP-21 track the Agency’s strategic framework to improve commercial motor vehicle safety by supporting its three core principles:
- Raise the bar to enter the industry and operate on the roads;
- Hold motor carrier and drivers to the highest safety standards to continue operations; and
- Remove the highest risk drivers, vehicles, and carriers from the roads and prevent them from operating.
Countries are implementing legislature and safety mandates that are raising the bar for transportation companies to improve their safety records and asset management processes. Predikto is working with some of the largest transportation companies in North America to facilitate the deployment of Predictive Analytics solutions to predict asset failures and prevent safety hazards. Predictive Analytics solutions can take into account historical maintenance records, prior asset failures, and current operating conditions of the equipment to identify “High Risk” situations and remove the highest risk drivers, vehicles, assets, and carriers from roads and railroad tracks.
In future posts we will be talking about specific sections of interest for predictive analytics, such as Transit Asset Management (Sec. 5326), Public Transportation Safety Program (Sec. 5329), and State of Good Repairs (sec. 5337).