Automation of Blast Furnaces at Tata Steel with NetBeans
JAXenter reports that the Automation Division of Tata Steel Ltd has developed a Level2 system Blast Furnace and implemented a H–Blast Furnace at Tata Steel Jamshedpur.
Blast Furnace Level2 system is a collection of mathematical & mass-energy balance models which, based on first principles, mathematical equations and numerical methods, simulate the blast furnace process in segments on real time basis. The models extract plant data like flow, temperature, pressure, distance, velocity etc from the field devices and convert them into trends using fundamental principles of physical laws. The Level2 system helps operators to visualize the process of the blast furnace and in turn assists them in operation with better control facilities.
Read more about Blast Furnace Level2 system at: https://jaxenter.com/netbeans/automation-blast-furnaces-tata-steel-netbeans
Machine Learning Will Help Us Fix What’s Broken Before It Breaks
Digital twins, exact virtual replicas physical devices, are computer models operating identically to the physical versions, able to detect problems before they have the chance to happen in the real world. Combined with predictive machine learning, the digital twins are hoped to reduce downtime resolving problems before they even occur.
However, as Big Think reminds us on their article on machine learning, there are still devices in service predating the notion of digital twins, especially in industrial settings. Luckily there are several companies developing bridge technologies that would bring the benefits of digital twins to devices without one. They are harnessing machine learning for analyzing data to pick up subtle variations from normal operation that may predict imminent malfunctions. Their approaches vary from analyzing sounds machines make to detecting changes in machine-produced vibrations.
Read more about how machine learning and AI can keep machines and industrial plants operating at: http://bigthink.com/robby-berman/machine-learning-will-help-us-fix-whats-broken-before-it-breaks
How Industrial IoT enables the factory of the future
Trillion-dollar projections on the expanding size of the market are urging companies to capitalize on the Industrial IoT. For many, however, it remains unclear how industries should apply IIoT to begin making the hyper-efficient and agile factory of the future a reality. Fabio Bottacci, founder and CEO of VINCI Digital and Industrial IoT Expert Contributor at the World Economic Forum and at the Brazilian Development Bank (BNDES), shares his insights on how Industrial IoT is already increasing operational efficiency, saving time and reducing cost.
As the Fourth Industrial Revolution transforms manufacturing and material handling, enterprises continue to look for ways to create value from converging technologies. But what are the steps that companies need to take to put together an effective agenda of action? Fabio Bottacci finds it essential that the implementation of industrial internet is incorporated into the company’s strategy and business development. In other words, chief executives must embrace change. “In order to advance decision-making on the correct level, CEOs must be included from the very beginning, possibly as initiative main sponsor. IT officers alone cannot drive real digital transformation,” says Bottacci.
Bottacci advises manufacturers to initiate the transformation by defining a specific set of goals, to be assessed and validated initially on a pilot project, before the implementation at scale of an end-to-end Industrial IoT solution. The next step is to deploy an industrial internet pilot in one facility, or on a specific production line, which will be used as a case study for learning how IoT works in this particular industrial environment. The pilot facility is then reworked and developed according to observations. After the test phase, it is easy for a company to apply the same principles, with proper adjustments, at scale to other facilities.
Bottacci uses the concept of flexible infrastructure to refer to how transformation can be simpler in certain contexts. “It is easier to justify large investments in industrial internet in environments where industrial internet is incorporated into production by transitioning directly to automated, advanced IIoT environments. The transition phase is less complicated when the existing infrastructure is light, because there are fewer things that must be accounted for in applying new solutions,” he explains.
A case in point is Romania, where the internet infrastructure is now top of the class in Europe. The Romanian infrastructure was created rather recently compared to more affluent European countries, and therefore, the entire web is more modern than that in Finland, for example.
Industrial internet in practice
“IIoT coupled with AI or ML turns maintenance into a dynamic, rapid and automated task.”
Bottacci emphasizes that applications of industrial IoT are already a reality. According to him, there are dozens of different use cases of IIoT in enterprises. “Companies are already developing IoT applications that work, and they have started making a difference. For example, transportation and warehousing benefit from automated vehicles and asset tracking. In manufacturing, predictive maintenance and asset performance management are key areas where industrial internet boosts value creation.”
Predictive maintenance keeps assets up and running, decreasing operational costs and saving companies millions of dollars. Data from IIoT-enabled systems – sensors, cameras, and data analytics enabled by powerful artificial intelligence (AI) or machine learning (ML) algorithms – helps to better plan maintenance, allowing manufacturers to service equipment before problems occur. “Data streaming from sensors and devices can be used to quickly assess current conditions, recognize warning signs, deliver alerts and automatically trigger appropriate maintenance processes. IIoT coupled with AI or ML thus turns maintenance into a dynamic, rapid and automated task,” Bottacci explains.
“Other potential advantages include increased equipment lifetime, increased plant safety and fewer accidents with negative impact on environment,” he adds.
The importance of edge analytics
“Companies have been proactive in moving the processing of IIoT to cloud services,” Bottacci notes. However, in his opinion, it is not necessarily a wise move to have everything in the cloud. During critical stages of the manufacturing process it is crucial that decisions can be made instantaneously. Here, manufacturers can benefit from edge analytics.
“Edge computing enables real-time analytics. Edge analytics is an approach to data collection and analysis where automated analytical computation is performed on data at a sensor, network switch or other device instead of waiting for the data to be sent back to a centralized data store. IIoT can be supplemented with Arduino-based, open-source computer hardware and software applications that allow some of the processing to take place on site, at the edge of the network and near the source of the data. Edge computing helps ensure that the right processing takes place at the right time, in the right place,” Bottacci explains. “Edge computing is a preferable option for the cloud in terms of security, as proprietary data is kept within the company firewall. Moreover, edge computing becomes vital when you need real-time analysis and automated action to save critical-mission production lines or facilities from potential heavy damages.”
Creating value with Industrial IoT
“There’s no value in the data without advanced algorithms of machine learning.”
Bottacci says that value can be created in surprisingly simple ways by putting data to work. As an example of enhancing safety and efficiency in material handling, he refers to a fleet management system in Silicon Valley. “Peloton Tech’s truck platooning system is a case study that illustrates how IIoT is already creating value. The system uses vehicle-to-vehicle communication to connect the braking and acceleration between two trucks. The lead truck controls the simultaneous acceleration and braking of the whole fleet, reacting faster than a human or even a sensor system could. What follows is a reduction in aerodynamic drag, which leads to companies saving around seven per cent in fuel cost. In terms of annual savings, this is a remarkable number,” says Bottacci.
In Europe, trucking companies such as Scania and Volvo Trucks have adopted IIoT fleet thinking. “It still takes courage to adopt innovations like these,” Bottacci admits. However, he recommends getting started quickly by building a case study of industrial internet and then working towards expanding IIoT to cover more and more of the industrial realm. “Companies should start seeing emerging technology like Industrial IoT not as a threat but as the only way to survive in a matter of a few years. That’s two or three years if you are an optimist, five to ten if you are more conservative,” estimates Bottacci.
In Bottacci’s view, the simple capacity of devices to seize data is not what the Industrial Internet of Things is essentially about. “Even if you have all the infrastructure and the technology to get the data – sensors, WiFi, the gateway, the cloud – and the capacity of analyzing the data, there’s no value in it without AI, more specifically advanced algorithms of machine learning.”
“IIoT is about AI or ML analyzing data in real time so as to make decisions and act, most of the times several days or even weeks before a potential issue. This process results in actual business outcomes,” Bottacci states. “Prescriptive analytics react autonomously, real-time: In a mission-critical situation, a prescriptive system will autonomously decide what to do. This is where edge analytics is imperative,” he explains. “My point is: You can’t consider industrial internet standalone. The real value comes from how companies use AI and ML-enabled IIoT solutions in analyzing and processing data.”
Fabio Bottacci works as an independent advisor. He is founder and CEO of VINCI Digital and an Industrial IoT Expert Contributor at the World Economic Forum and at BNDES, the Brazilian Development Bank.
Unifying industrial IoT technologies with monitoring
While the majority of industrial companies have multiple systems and technologies managing various components of their operations, only a few have been able to aggregate the data from all of these systems together to drive business prosperity. When brought together, the data from these disparate systems can produce insights across all aspects of the business.
“The teams and upper management of these industrial organisations could – with a unified monitoring tool – see every link in the supply chain and their products’ evolution from early stages right through to delivery,” Paessler AG’s APAC sales director Andrew Timms told IoT Hub. Besides operations-wide oversight, monitoring tools can also be used for the management of particular components of the value chain.
Read more about aggregating data from RFID, SCADA, IT and other sources at: https://www.iothub.com.au/news/unifying-industrial-iot-technologies-with-monitoring-458258
Looking for the human-machine touch
Digital technology is fast changing the way vehicles are built, but the pace of change varies according to different manufacturers and production processes. Above all, the importance of human workers has been central to the decision process for new technology – and looks set to remain so in the future.
According to Automotive Logistics, experts who spoke at automotiveIT Forum – Production and Logistics, which took place during the recent Hannover Messe, stressed that digitalization starts on the shop floor. Implementing logistics automation and support technology needs to be done with workers in mind – including their safety and comfort, but also their skills. For instance, Dr. Sabine Pfeiffer, professor of sociology at the University of Hohenheim, noted that the industry tends to focus on university graduates or consultancies, “but if you work with the experience and skills on the shop floor, you will get great results.”
Read more on how to begin disruption at the shop floor level: http://automotivelogistics.media/intelligence/looking-human-machine-touch
Image credit: Zapp2Photo / Shutterstock.com
Pushing IIoT predictive maintenance forward: two challenges to overcome
Enabled by wireless technology and connected devices, communication between machines and human technicians is fueling a shift from preventative to predictive maintenance. To push IIoT predictive maintenance technologies up the slope of enlightenment and spark mainstream adoption and success, two major challenges must be overcome: the challenge to obtain high quality data from industrial machines, and that to fuse sensor data with maintenance activities.
An article in Reliabilityweb offers solutions ranging from deep learning algorithms to tapping into the intuitive human capacity of sound-based diagnosis.
Read more about ways to overcome IIoT maintenance challenges and combine deep learning and human input: http://reliabilityweb.com/articles/entry/pushing-iiot-predictive-maintenance-forward-two-challenges-to-overcome
Image credit: Zapp2Photo / Shutterstock.com
How the Internet of Things impacts supply chains
Enterprise resource planning and supply chain management (SCM) have gone hand-in-hand for quite some time, but the IoT revolution will allow those solutions to be enhanced by intelligently connecting people, processes, data, and things via devices and sensors.
“Think of it as SCM 2.0,” writes Udaya Shankar, Vice President and Head of Internet of Things for Xchanging, a business process service provider for the global insurance industry. According to Shankar’s article in Inbound Logistics, this deeper intelligence can come to life in many different ways when it comes to supply chain data and intelligence – from the automation of the manufacturing process to improved visibility within the warehouse.
One area that Shankar believes will play a prominent role in the future supply chain, as it’s impacted by IoT, is in-transit visibility. “The logistics ecosystem has many players, and thus, many moving parts. Products are handled and transferred between the manufacturer, suppliers, the distribution center, retailer, and customer.”
Read more about how IoT can help supply chain professionals at:
Image credit: Lightspring / Shutterstock.com
How to use plant floor data to make smart strategic business decisions
In the case of HK Metalcraft, a manufacturer specializing in precision metal stampings, IoT has made possible the harnessing of plant floor data. “Connecting the plant floor to HK’s business operations through cloud ERP turned that data into actionable information,” according to an article published by Industry Week. The piece is based on a White Paper published by US software company Plex.
When coupled with what happens on a plant floor, a cloud ERP solution enables “the kind of insight and control manufacturers need to make critical business decisions.” Cloud ERP has allowed HK Metalcraft to manage the downtime of operators and see everything from the direct overhead down to the specific amount of time that each operator has spent doing a specific job. “Now not only does HK Metalcraft know exactly what caused the downtime but they also have actionable data to improve processes and overall equipment effectiveness.”
Read more about how HK Metalcraft turned data into actionable information at: http://www.industryweek.com/cloud-computing/how-use-plant-floor-data-make-smart-strategic-business-decisions
Image credit: Pavel L Photo and Video/ Shutterstock.com
Top 10 ways integration will transform manufacturing in 2017
“Enabling a faster pace of innovation in manufacturing starts by using systems and process integration as a growth catalyst to profitably grow,” writes Louis Columbus, Vice President of Marketing at iBASEt.
In Columbus’ article for Enterprise Irregulars, he highlights the importance of real-time data for both manufacturers and customers. He also mentions how the integration of traditional IT and manufacturing systems are vital in order for the potential advantages of Industry 4.0 to be fully realized.
“The key to revitalizing existing production centers and getting them started on the journey to becoming smart factories depends on the real-time integration of IT and manufacturing systems,” Columbus continues. Manufacturers should also expect the importance of IoT generated sensor data, combined with advanced analytics, to keeping increasing in 2017.
Read more about how integration powers manufacturing innovation at:
IoT spending 2017-2020: Internet of Things industry drivers and investments
According to i-Scoop, manufacturing, transportation and utilities are the industries “poised to invest the most in IoT until 2020”. Though currently we are seeing a lot of investments in Consumer Internet of Things (CIoT), it is expected that by 2020 these investments will decrease. The article highlights aspects of the IDC Worldwide Semiannual Internet of Things Spending Guide.
“In the leading IoT industry, manufacturing, operations by far represent the main spending use case ($102.5 billion in 2016 on the mentioned total of $178 billion), outperforming other manufacturing IoT use cases such as production asset management and maintenance and field service. The only exception is the EMEA region, where freight monitoring (transportation) is the main use case, followed by manufacturing operations,” according to the IDC report.
Read more on IIoT investments and patterns per industry and cross-industry at: http://www.i-scoop.eu/iot-spending-2020/
Image credit: Hamik / Shutterstock.com