Achieving the convergence of IT and OT Industry 4.0 is no longer a distant vision

For many manufacturers, concepts such as Industrial Internet of Things (IIoT), Information Physics Systems (CPS), cloud robots, fog computing, and big data have begun to connect with their vision of smart factories. The smart factory connects the digital world of information technology (IT) with the physical world of operational technology (OT), which is the integration of IT and OT.

For the future factory, Industry 4.0 is no longer a distant vision, it is here, right now. Today, robotic networks are connected to the cloud and can provide a lot of high quality data. Manufacturers are using these information channels to streamline asset management and maintenance, maximize equipment and process efficiency, and improve product quality.

Predictive maintenance and reduced downtime

General Motors is deploying the infrastructure of the Internet of Things and Industry 4.0 into manufacturing. Its robot supplier and strategic partner, Fanuc America, is helping GM to lay a solid foundation for smart manufacturing. GM, FANUC and Cisco have jointly developed the Zero Downtime Function (ZDT) solution, which uses a cloud-based software platform to analyze data collected by general plant robots to identify potential sources of production downtime. problem.

A cloud-connected welding robot that helps realize the vision of Industry 4.0 by monitoring potential downtime issues and facilitating predictive maintenance.

In the automotive industry, every 60 or 90 seconds, a whole vehicle goes offline from the assembly line, and every minute of downtime will cost the manufacturing company $20,000. A downtime event could result in millions of dollars in losses. When the production line suddenly stops, it may affect the entire supply chain, further increasing losses. These delays will further extend down to customers, car dealers, fleet users and consumers who buy cars.

“We have taken the initiative to take some time to try to better predict and maintain the health of our manufacturing equipment,” said Marty Linn, chief engineer of GM's Detroit automation technology and robotics. “We work with FANUC to discuss what we can do to avoid accidents during the production process. This is not a great vision for Industry 4.0, but what we can do in terms of predictive maintenance to actually eliminate Unpredictable downtime at the factory."

In 2014, GM launched a ZDT pilot project. A strategic partnership between General Motors and Fanuc is a key factor in success. The history of the cooperation between the two companies dates back to the early 1980s, when GM partnered with Japanese robot manufacturers to form a joint venture to form the General Fanuc Robotics Company to develop and sell robots in the United States. The company later became independent, but the relationship between the two parties still exists.

“As we said, we are integrating robots. As we are constantly introducing new products and new projects, robots and systems from integrators are delivered to the factory every day,” says Linn.

The Cloud Connect software platform collects and analyzes large amounts of data from thousands of robots to prevent downtime, predict maintenance, and optimize process efficiency.
Achieve the expected return on investment

The impact of ZDT on the corporate floor is continuous. Linn said that since the project's inception, GM has successfully avoided more than 100 major unplanned downtimes.

Unexpected downtime of 6 to 8 hours can be avoided depending on the type of failure. For any facility, it's a big deal, especially in large trucks and SUV plants, where every downtime is important.

A software platform ready for Industry 4.0 that connects the robot to the cloud and provides performance data at your fingertips.

As thousands of robots connect to the cloud and establish communication with them, GM will receive the expected return on investment in the near future.

“This is the use of technologies such as big data, the Internet of Things, new algorithms, and computer functions. All of these emerging technologies have evolved over the past few years and are being applied in the most efficient way,” says Linn. This allows manufacturing companies to achieve predictive maintenance and effectively reduce downtime incidents, even abandoning existing maintenance plans until they are needed.

On-demand maintenance plan

In the initial phase, GM progressed slowly, and in the first two years, only a total of thousands of robots were connected. However, by 2017, more than 8,500 FANUC robots were connected. “We started slowly when we started deploying,” Linn said. “After discovering the problem, we will intervene and replace the parts. Then we will study these parts. It is very certain that we have been able to verify and confirm that these parts will fail. Dealing with the problem parts that may cause downtime is based on So, when we can reduce irregular maintenance events, this makes every employee very excited."

General Motors began using ZDT to develop on-demand maintenance plans rather than relying on routine maintenance plans. “For example, the robot is designed to perform routine maintenance after 1000 hours of operation. We used to plan to maintain the robot at that point in time,” Linn said. “But it actually works for 1250 hours, until it is needed. It's maintenance. So, now we're working hard to get rid of a fixed maintenance plan, but plan on demand. This is the most important way you can find a way to bring huge cost savings.

Industrial IoT software provides robot asset information, status monitoring and maintenance alerts on any device, anytime, anywhere.

Machine learning

ZDT is not only suitable for robots, but also for processing equipment, as well as processes directly controlled by robots such as welding, spraying and some dispensing applications. “By looking at the air pressure sinking pressure, dispensing the speed of the paint actuator, and viewing a large number of coating processes and parameters, we were able to monitor the health of the equipment and understand the quality of the work,” says Linn.

In automotive paint shops, the quality of finished products is of paramount importance. All FANUC paint robots have ZDT capabilities, which means they can monitor a variety of functions, including the health status of paint cans, spray nozzles, regulators and drives.

If you count the total number of moving parts associated with automotive painting, you will find that each robot has more than 200 moving parts. A significant number of these moving parts are associated with specific process devices such as control guns, regulators, and pressure. If any of these devices fail prematurely, quality problems may occur or production downtime may result.

Currently, GM has more of a ZDT as a predictive maintenance tool than an in-process adaptive tool. But as technology evolves, more and more data is collected and analyzed, and algorithms become more complex. Users can discover how to use machine learning as an adaptive tool for real-time process improvement.

“We want to expand this strategy to make the equipment intelligent, self-diagnosed, and notify us when performance changes so that we can make adjustments or repairs as needed,” says Linn.

Tailored

FANUC's analytics solution monitors more than 10,000 cloud-connected robots in customers' facilities around the world, and this number is growing every day. Although initially used only in the automotive industry, it is reported that Fanuc has provided support for non-automotive customers to release software and hardware at the end of 2017.

For small manufacturers, installing software and hardware settings requires plug-and-play because they don't have the support of a dedicated IT department. For example, if applied to a typical small industrial manufacturer with 3 robots, it needs to be "cropped".

Fanuc and Cisco plan to use this data communications highway developed specifically for ZDT to connect to devices other than robots. As part of the FANUC Intelligent Edge Link and Drive (FIELD) system, ZDT provides an open software platform that allows advanced analysis and deep learning of sensors used in CNC machine tools, robotics, peripherals, and automation systems. Based on edge computing technology, the site processes large amounts of data at manufacturing points at the edge of the network, minimizing the amount and cost of shared data.

With the ZDT cloud solution, data flows from devices across the production floor to the cloud, where there may be some delay or delay. The benefit of using the on-site platform to receive production floor data is that it can respond to events in real time, which is what the site has to do. It is an open software platform that can be installed on computer hardware and then allows customers to access data obtained from robots, programmable logic controllers (PLCs), or machine tools, and analyze them in real time. It can even change production based on behavior. This is the good value that real-time machine learning can provide.

For the automation industry, Industry 4.0 is no longer an unreachable dream. More and more robot manufacturers are launching their own IoT solutions to embrace the level of connectivity required by Industry 4.0.

Robot data at your fingertips

Kuka Connect is a cloud-based software platform that allows customers to access and analyze KUKA robot data on any device, anytime, anywhere. The solution provides three main functions: asset information management, status monitoring, and maintenance alerts.

“If you are a large-scale OEM, there may be thousands of robots in operation at the same facility,” said Andy Chang, director of product marketing at KUKA USA. “Today, their asset information management approach is a manually maintained Microsoft Excel spreadsheet. The information on the spreadsheet may or may not be accurate, so they may not actually know which type of robot they have.”

If there is no correct asset information, it will affect the maintenance of the robot throughout its life cycle. “You can easily find thousands of robots around the plant and view them one by one, seeing their commissioning time, checking the serial number, and the software currently installed, without having to walk to the machine in person,” says Chang.

For condition monitoring, Kuka Connect is designed to provide specific robot key performance indicators (KPIs) to help technicians and maintenance personnel measure the performance of the robot.

“We provide temperature maps for all axes of the robot,” Chang said. Therefore, if the production personnel or maintenance personnel observe that the temperature trend of a certain axis has started to rise last week, it may mean the following problems. For example: the gearbox overheats for any reason; perhaps the load must change; or the target picked up by the robot may not be the scope of the machine design.

Kuka Connect is available for desktops as well as mobile devices, smartphones, tablets or any device that supports web browsers. Intuitive dashboards help users visualize data to specific criteria. Whether you're optimizing your maintenance schedule or managing spare parts inventory, all the data is at your fingertips, so you can anticipate potential downtime and take steps to fix the problem before it goes down.

“Now, KUKA connectivity provides information in two ways,” says Chang. “One is a very straightforward approach. When a controller error message occurs, we provide the user with real-time notification of the error code and error description, so it’s very Dynamic. The second way is relatively indirect. We submit the data to the end user, who then needs to interpolate the data to get meaningful data for their robots, production lines, and factories."

The software platform not only interfaces with the robot, but also monitors automation devices controlled by the robot controller, such as welding guns or glue guns. If the robot is on the track, there may even be an additional shaft.

“Any information controlled or assisted by a robot is part of the platform,” Chang said. “This is what we are currently doing. This feature enables visualization of robot data and specific process data, so end users can not only understand the mechanical health of the machine, but also the key performance indicators of the process itself.”

Helping Industry 4.0 Talent Development

To truly realize the vision of Industry 4.0 and smart factories, we need to bridge the growing technology gap by spanning a large pool of talent across multiple geographies and industries. As the world's leading industrial education technology training organization, Festo DidacTIc is helping users build an industry 4.0 talent pool and train engineers to acquire the new skills needed for future plants.

Ted Rozier, engineering development manager at Festo DidacTIc USA, said that there may be nearly 300,000 US manufacturing jobs that lack suitable candidates to find suitable candidates. And this number is expected to continue to grow.

“It’s important to familiarize students with the complete combination of automation hardware and software,” says Rozier. They need to understand the integration process between robots and PLCs, and how the Internet of Things improves the process. This is a common practice in Europe, and we hope to improve This type of training is well known in North America.

Rozier emphasized the importance of multidisciplinary learning, especially focusing on mechatronics. “In order to bring vitality to Industry 4.0, the Internet of Things must thrive. To do this requires a strong IT background and a strong mechatronics background. We have the opportunity to nurture not only understand but also from the management to the workshop. Layers, from the IT layer down to the sensor and other aspects affect the automated manufacturing process, helping the robot to make the decision. This is an important skill development."

A modular network physics learning platform that simulates workstations in real-world production facilities, teaching students the multidisciplinary skills necessary for Industry 4.0 and smart factories.

Learning factory module

Festo DidacTIc offers the “Learning Factory Module” for industrial training in practical mechatronics, control technology and automation technology. The system starts with a module: Single Project Workstation I4.0 and is used to train the basic principles of control technology. Several modules can then be added to create a complete learning network-physical (CP) plant that includes a realistic industrial pallet cycle system and an autonomous mobile robot to connect to different workstations.

The system is modular, so each workstation can be added, removed, and moved as learning needs change. Training topics include: PLC project engineering, working with Human Machine Interface (HMI) and Radio Frequency Identification (RFID) sensors, debugging Web servers and TCP/IP and OPC-UA interfaces, energy monitoring and management, and intelligent process data modules, enterprises Resource Planning (ERP) systems, Manufacturing Execution Systems (MES), and rapid prototyping work together.

Some manufacturing and STEM tracks community colleges are using Festo's CP factory modules to train students so they can get to work immediately after graduation. The York Institute of Technology in Rock Hill, South Carolina, USA has a room dedicated to the CP plant, with approximately six modules. Like the Mercedes-Benz facility in Vance, Alabama, Festo learning equipment is being used to improve workers' knowledge. We must be prepared for new ways of interacting with people, machines and data in a highly interconnected world. Now, there are steps you can take to prepare for Industry 4.0 and Smart Factory.

The Guardian S robotic mobile IoT platform includes a smart, remotely operated robot for unpredictable and unstructured environments such as stair climbing or uneven tilting.

Mobile robot integrated with IIoT platform

Most robots are designed for specific functions. They focus on doing one thing, repeating it, and doing it well. On the other hand, mobile industrial Internet of Things (IIoT) robots are more generalists. In a way, it can be seen as an unmanned autonomous vehicle (UAV) or even a ground drone. There is a robot that looks like a snake, which makes it particularly interesting.

The "snake robot" has been around for about a decade and was originally used for search and rescue operations in complex terrain environments, which may include mud-filled environments, earthquakes or mine collapse sites. Sarcos RoboTIcs recently released its Guardian S robotic mobile IoT platform, a smart, remotely operated robot that is used primarily in unpredictable and unstructured environments. The cost of a basic unit is about $60,000, or a service contract can be signed for $2,000 per month.

The robot integrates the Microsoft Azure cloud computing platform with the Microsoft Azure IoT suite and uses Windows 10 as a tablet controller. Cloud computing platforms enable customers to collect, store, and analyze sensor data in challenging environments, or deploy fixed sensors in areas where it is not feasible.

“Under a wide open environment, drones have done an incredible job in collecting useful data, but we have found that in some application environments there is a higher demand for data collection, such as in closed or closed Space, or the opportunity to collect data for hours, not minutes, or to collect data in the vicinity of the sensor or in contact with the surface.” Ben Wolff, Chairman and CEO of Sarcos Robotics Say.

The Guardian S robot is like a versatile, wirelessly controlled, unmanned ground vehicle that can carry multiple sensor payloads as a mobile IoT platform. The 13.5-pound robot can be operated remotely and across challenging terrain, including stairs, culverts, pipes, water tanks, vertical ferromagnetic surfaces and confined spaces, while also facilitating two-way real-time video, voice and data communications.

The combination of IoT sensors and cloud services is very valuable for assessing the performance and predictions required for the various industrial machines being deployed. Robots equipped with IoT platforms increase the efficiency of the cloud by leveraging Azure cloud computing capabilities and analytics to collect and analyze data related to the environment surrounding the robot. (Author: Tanya M. Anandan)

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