For many manufacturers, concepts such as Industrial Internet of Things (IIoT), Information Physics System (CPS), cloud robotics, fog computing, and big data have begun to be closely linked to the vision of their 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 future factories, Industry 4.0 is no longer a distant vision. It is here, right now. Nowadays, the robot network has been connected to the cloud and can provide a lot of high-quality data. Manufacturers are using these information channels to simplify asset management and maintenance, maximize the efficiency of equipment and processes, and improve product quality.
Predictive Maintenance and Reduced Downtime <br> General Motors is deploying the Internet of Things and Industry 4.0 infrastructure to manufacturing. Its robot supplier and strategic partner, Fanuc USA, is helping GM lay a solid foundation for smart manufacturing. General Motors, Fanuc, and Cisco jointly developed a Zero Downtime Function (ZDT) solution that uses a cloud-based software platform to analyze data collected by general plant robots to identify potential potential production outages problem.
In the automotive industry, there is an entire vehicle that goes off the assembly line every 60 or 90 seconds. Every minute of downtime will cost the manufacturing company $20,000. An outage may result in losses of millions of dollars. When the production line suddenly stops, it may affect the entire supply chain, further aggravating the losses. These delays will further extend down to customers, car dealers, fleet users, and consumers who buy cars.
“We have taken proactive steps for a while to try to better predict and maintain the health of our manufacturing facilities,†said Marty Linn, chief engineer at GM’s Detroit Automation Technology and Robotics. “We are working with FANUC to discuss what we can do to avoid accidents in the production process. This is not the great vision of Industry 4.0. It is about what we can do to predictably maintain and eliminate it.†Unpredictable downtime at the factory."
In 2014, GM launched a ZDT pilot project. The strategic partnership between General Motors and Fanuc is a key factor for success. The history of the cooperation between the two companies can be traced back to the early 1980s, when General Motors and Japanese robot manufacturers established a joint venture to form General Motors and Fanuc Robotics to develop and sell robots in the United States. The company later became independent, but the cooperation 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 arrive at the factory every day," said Linn.
Realize expected return on investment
The impact of ZDT on the company's workshop is continuous. Linn said that since the establishment of the project, GM has successfully avoided more than 100 major unplanned downtimes.
Depending on the type of failure, you can avoid unplanned downtime from 6 to 8 hours. For any facility, it is a big event, especially in large trucks and SUV factories. Every downtime event is very important.
With thousands of robots connected to the cloud and establishing communications with them, GM soon gained the expected return on investment.
“This is the use of technologies such as Big Data, Internet of Things, new algorithms, computer functions, etc. All these emerging technologies have achieved significant growth over the past few years and have been applied in the most effective way,†said Linn. This allows manufacturers to achieve predictive maintenance, effectively reduce downtime, and even abandon the original maintenance plan, and take measures until needed.
On-demand maintenance program <br> In the initial stage, General Motors progressed slowly. In the first two years, only a few thousand robots were connected. However, by 2017, there will be more than 8,500 FANUC robots connected together. "At the beginning of deployment, we are making slow progress," 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. Deal with the problem parts that may cause downtime. Therefore, when we were able to reduce the occasional maintenance incident, this made everyone excited."
General Motors began using ZDT to develop an on-demand maintenance program rather than relying on routine maintenance programs. "For example, the robot was designed to perform routine maintenance after 1000 hours of operation. We previously planned to maintain the robot at this point in time, according to the plan." Linn said, "But it can actually work continuously for 1250 hours until it is needed. It is maintenance, so now we are trying to get rid of fixed maintenance plans, but plan on-demand, which is the most important way you can find a way to bring about huge cost savings.
Machine learning
ZDT applies not only to robots, but also to processing equipment, as well as processes directly controlled by robots, such as welding, spraying, and some dispensing applications. “By looking at the air pressure sink pressure, assigning paint actuators, and looking at a large number of coating processes and parameters, we were able to monitor the health of the equipment and understand the quality of work,†said Linn.
In the automotive paint shop, the quality of the finished product is crucial. All Fanuc paint robots have the ZDT function, which means that they can monitor various functions including the status of paint cans, spray nozzles, regulators and actuators.
If you count the total number of moving parts related to car 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 one of these devices fails prematurely, there may be quality issues or production downtime.
Currently, General Motors uses ZDT more as a predictive maintenance tool than an in-process adaptive tool. However, with the development of technology, more and more data have been collected and analyzed, and algorithms have become more complex. Users can discover how to use machine learning and how it becomes an adaptive tool for real-time process improvement.
"We hope to expand this strategy, make the equipment smart, self-diagnose, and notify us when the performance changes, so that adjustments or repairs can be made as needed," said Linn.
Tailored <br> Fanuc analysis solution, we are monitoring more than 10,000 customers in connection with the cloud robot in facilities around the world, and the numbers are growing every day. Although it was initially used only in the automotive industry, it was reported that FANUC had provided support for non-automobile 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 do not have the support of a dedicated IT department. For example, if it is applied to a general small-scale industrial manufacturer with 3 robots, it needs to be “croppedâ€.
Fanuc and Cisco plan to use this data communications highway developed specifically for the ZDT to connect other devices beyond the robot. 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, robots, peripherals, and automation systems. Field calculation technique based on edge, in the manufacturing process a large amount of data in the network edge point, to minimize the number and cost of sharing data.
ZDT use cloud solutions, data flows from the cloud production equipment throughout the plant, where there may be some delay or lag. The advantage of using on-site platforms to receive production floor data is that it can respond to events in real time, which is what the field does. 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 a distant dream. More and more robot manufacturers have launched 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 larger OEM, there may be thousands of robots in operation in the same facility," said Andy Chang, director of product marketing at KUKA USA. “Today, their asset information management approach is to manually maintain Microsoft Excel spreadsheets. The information on the spreadsheet may be accurate or inaccurate, so they may not actually know what type of robot they have.â€
Without the correct asset information, the maintenance of the robot will be affected throughout the entire life cycle. “You can easily find thousands of robots around the plant and browse them one by one to see their commissioning time, check the serial number, and the currently installed software without having to physically walk to the edge of the machine,†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 robot's performance.
"We provide a temperature map of all axes of the robot," Chang said. So, if the production or service personnel observed temperature trend chart has a shaft began to rise last week, that could mean the emergence of the following questions. For example: overheating of the gearbox for any reason; perhaps the load must be changed; or the target that the robot picks up may not be the scope of the machine design.
Kuka Connect for desktop and mobile devices, smart phone, tablet or any web-enabled device with a browser. Intuitive dashboards help users visualize data based on specific criteria. Whether you are optimizing a maintenance plan or managing spare parts inventory, all data is at your fingertips so you can predict potential downtime and take steps to fix problems before downtime.
“Now, KUKA connections provide information in two ways,†said Chang. “One is a very straightforward approach. When a controller error message comes up, we provide users with real-time notification of error codes and error descriptions, so it’s very The second way is relatively indirect. We submit the data to the end user, and then they need to interpolate the data to get meaningful data about their robots, production lines, and factories."
The software platform can not only interface with the robot, but also can monitor automation equipment controlled by the robot controller, such as welding guns or glue guns. If the robot is in orbit, there may even be an additional axis.
"Any robot controlled or assisted information is part of the platform," said Chang. "This is what we are currently working on. This function can realize the function of visualizing robot data and specific process data, so that end users can not only understand the mechanical health of the machine, but also understand the key performance indicators of the process itself."
Helping Industry 4.0 Talent Development <br> To truly realize the vision of Industry 4.0 and smart factories, we need to bridge the growing technology gap across a large pool of talent in multiple geographic regions and industries. As the world’s leading industrial education and technical training institution, Festo Didactic is helping users build Industry 4.0 talent pools and train engineers to master the new skills needed for future factories.
Ted Rozier, project development manager for Festo Didactic America, said that there may be nearly 300,000 U.S. manufacturing jobs that could not find a suitable candidate because of a lack of qualified candidates. And this figure is expected to continue to grow.
“It is very important for students to become familiar with the complete combination of automation hardware and software.†said Rozier. They need to understand the integration process of robots and PLCs and become familiar with how the Internet of Things improves the entire process. This is a common practice in Europe and we hope to increase The popularity of such training in North America.
Rozier emphasized the importance of multidisciplinary learning, especially the importance of mechatronics. "In order to bring dynamism 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 understanding, but also from management to the workshop. Layers, down from the IT layer down to sensors and other aspects affect the automated manufacturing process and help robots make decision-making talent. This is an important skill development."
Learning factory module
Festo Didactic provides "learning factory modules" for industrial training in practical mechatronics, control technology and automation technology. The system starts with a module: single project workstation I4.0, which is used to train the basic principles of control technology. You can then add several modules to create a complete learning network - entity (CP) facility, which includes a realistic industrial pallet circulation system and an autonomous mobile robot to connect to a different workstation.
The system is modular, so you can add, delete, and move workstations based on changes in learning needs. 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 smart process data modules, companies Resource Planning (ERP) systems, Manufacturing Execution Systems (MES) and rapid prototyping work together.
Some community colleges in manufacturing and STEM tracks are using Festo's CP factory module to train students so that they can immediately start working after graduation. The York Institute of Technology in Rock Hill, South Carolina, USA, has a room dedicated to the CP factory, with approximately six modules. At the Mercedes-Benz plant in Vance, Alabama, Festo is learning to use equipment to increase workers' knowledge. We must prepare ourselves for new ways of interacting people, machines and data in a highly connected world. Now you can take steps to prepare for Industry 4.0 and Smart Factory.
Mobile Robots with Integrated IIoT Platform <br> 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 versatile. To some extent, it can be seen as an unmanned self-driving vehicle (UAV), or even a ground drone. The shape of a robot is similar to a snake, which makes it particularly interesting.
The "snake shaped robot" has been around for about 10 years and was originally used for search and rescue operations in complex terrain environments, which may include muddy environments, earthquakes or mine collapse sites. Sarcos Robotics recently announced its Guardian S robot mobile IoT platform, which is a smart, remotely operated robot designed for use in unpredictable and unstructured environments. The cost of a basic device is approximately $60,000, or a service contract can be signed for $2,000 per month.
The robot integrates the Microsoft Azure cloud computing platform and 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.
"In an open environment, drones have done an incredible job of collecting useful data, but we have found that there are higher demands for data collection in certain application environments, such as closed or closed. Space, or the need to collect data for hours, not minutes, or the opportunity for data to only collect sensors near or in contact with the surface, says Ben Wolff, Chairman and CEO of Sarcos Robotics Say.
The Guardian S robot is like a multipurpose, wirelessly controlled, driverless ground vehicle that can carry multiple sensor payloads as a mobile IoT platform. This 13.5-pound robot can remotely operate and span challenging terrain including stairs, culverts, pipes, 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 of the various industrial machines deployed and for the maintenance needed to predict. Robots equipped with IoT platforms improve cloud efficiency by using Azure cloud computing capabilities and analytics to collect and analyze data related to the robot's surroundings.
(Author: Tanya M. Anandan)
For future factories, Industry 4.0 is no longer a distant vision. It is here, right now. Nowadays, the robot network has been connected to the cloud and can provide a lot of high-quality data. Manufacturers are using these information channels to simplify asset management and maintenance, maximize the efficiency of equipment and processes, and improve product quality.
Predictive Maintenance and Reduced Downtime <br> General Motors is deploying the Internet of Things and Industry 4.0 infrastructure to manufacturing. Its robot supplier and strategic partner, Fanuc USA, is helping GM lay a solid foundation for smart manufacturing. General Motors, Fanuc, and Cisco jointly developed a Zero Downtime Function (ZDT) solution that uses a cloud-based software platform to analyze data collected by general plant robots to identify potential potential production outages problem.
In the automotive industry, there is an entire vehicle that goes off the assembly line every 60 or 90 seconds. Every minute of downtime will cost the manufacturing company $20,000. An outage may result in losses of millions of dollars. When the production line suddenly stops, it may affect the entire supply chain, further aggravating the losses. These delays will further extend down to customers, car dealers, fleet users, and consumers who buy cars.
“We have taken proactive steps for a while to try to better predict and maintain the health of our manufacturing facilities,†said Marty Linn, chief engineer at GM’s Detroit Automation Technology and Robotics. “We are working with FANUC to discuss what we can do to avoid accidents in the production process. This is not the great vision of Industry 4.0. It is about what we can do to predictably maintain and eliminate it.†Unpredictable downtime at the factory."
In 2014, GM launched a ZDT pilot project. The strategic partnership between General Motors and Fanuc is a key factor for success. The history of the cooperation between the two companies can be traced back to the early 1980s, when General Motors and Japanese robot manufacturers established a joint venture to form General Motors and Fanuc Robotics to develop and sell robots in the United States. The company later became independent, but the cooperation 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 arrive at the factory every day," said Linn.
Realize expected return on investment
The impact of ZDT on the company's workshop is continuous. Linn said that since the establishment of the project, GM has successfully avoided more than 100 major unplanned downtimes.
Depending on the type of failure, you can avoid unplanned downtime from 6 to 8 hours. For any facility, it is a big event, especially in large trucks and SUV factories. Every downtime event is very important.
With thousands of robots connected to the cloud and establishing communications with them, GM soon gained the expected return on investment.
“This is the use of technologies such as Big Data, Internet of Things, new algorithms, computer functions, etc. All these emerging technologies have achieved significant growth over the past few years and have been applied in the most effective way,†said Linn. This allows manufacturers to achieve predictive maintenance, effectively reduce downtime, and even abandon the original maintenance plan, and take measures until needed.
General Motors began using ZDT to develop an on-demand maintenance program rather than relying on routine maintenance programs. "For example, the robot was designed to perform routine maintenance after 1000 hours of operation. We previously planned to maintain the robot at this point in time, according to the plan." Linn said, "But it can actually work continuously for 1250 hours until it is needed. It is maintenance, so now we are trying to get rid of fixed maintenance plans, but plan on-demand, which is the most important way you can find a way to bring about huge cost savings.
Machine learning
ZDT applies not only to robots, but also to processing equipment, as well as processes directly controlled by robots, such as welding, spraying, and some dispensing applications. “By looking at the air pressure sink pressure, assigning paint actuators, and looking at a large number of coating processes and parameters, we were able to monitor the health of the equipment and understand the quality of work,†said Linn.
In the automotive paint shop, the quality of the finished product is crucial. All Fanuc paint robots have the ZDT function, which means that they can monitor various functions including the status of paint cans, spray nozzles, regulators and actuators.
If you count the total number of moving parts related to car 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 one of these devices fails prematurely, there may be quality issues or production downtime.
Currently, General Motors uses ZDT more as a predictive maintenance tool than an in-process adaptive tool. However, with the development of technology, more and more data have been collected and analyzed, and algorithms have become more complex. Users can discover how to use machine learning and how it becomes an adaptive tool for real-time process improvement.
"We hope to expand this strategy, make the equipment smart, self-diagnose, and notify us when the performance changes, so that adjustments or repairs can be made as needed," said Linn.
Tailored <br> Fanuc analysis solution, we are monitoring more than 10,000 customers in connection with the cloud robot in facilities around the world, and the numbers are growing every day. Although it was initially used only in the automotive industry, it was reported that FANUC had provided support for non-automobile 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 do not have the support of a dedicated IT department. For example, if it is applied to a general small-scale industrial manufacturer with 3 robots, it needs to be “croppedâ€.
Fanuc and Cisco plan to use this data communications highway developed specifically for the ZDT to connect other devices beyond the robot. 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, robots, peripherals, and automation systems. Field calculation technique based on edge, in the manufacturing process a large amount of data in the network edge point, to minimize the number and cost of sharing data.
ZDT use cloud solutions, data flows from the cloud production equipment throughout the plant, where there may be some delay or lag. The advantage of using on-site platforms to receive production floor data is that it can respond to events in real time, which is what the field does. 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 a distant dream. More and more robot manufacturers have launched 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 larger OEM, there may be thousands of robots in operation in the same facility," said Andy Chang, director of product marketing at KUKA USA. “Today, their asset information management approach is to manually maintain Microsoft Excel spreadsheets. The information on the spreadsheet may be accurate or inaccurate, so they may not actually know what type of robot they have.â€
Without the correct asset information, the maintenance of the robot will be affected throughout the entire life cycle. “You can easily find thousands of robots around the plant and browse them one by one to see their commissioning time, check the serial number, and the currently installed software without having to physically walk to the edge of the machine,†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 robot's performance.
"We provide a temperature map of all axes of the robot," Chang said. So, if the production or service personnel observed temperature trend chart has a shaft began to rise last week, that could mean the emergence of the following questions. For example: overheating of the gearbox for any reason; perhaps the load must be changed; or the target that the robot picks up may not be the scope of the machine design.
Kuka Connect for desktop and mobile devices, smart phone, tablet or any web-enabled device with a browser. Intuitive dashboards help users visualize data based on specific criteria. Whether you are optimizing a maintenance plan or managing spare parts inventory, all data is at your fingertips so you can predict potential downtime and take steps to fix problems before downtime.
“Now, KUKA connections provide information in two ways,†said Chang. “One is a very straightforward approach. When a controller error message comes up, we provide users with real-time notification of error codes and error descriptions, so it’s very The second way is relatively indirect. We submit the data to the end user, and then they need to interpolate the data to get meaningful data about their robots, production lines, and factories."
The software platform can not only interface with the robot, but also can monitor automation equipment controlled by the robot controller, such as welding guns or glue guns. If the robot is in orbit, there may even be an additional axis.
"Any robot controlled or assisted information is part of the platform," said Chang. "This is what we are currently working on. This function can realize the function of visualizing robot data and specific process data, so that end users can not only understand the mechanical health of the machine, but also understand the key performance indicators of the process itself."
Helping Industry 4.0 Talent Development <br> To truly realize the vision of Industry 4.0 and smart factories, we need to bridge the growing technology gap across a large pool of talent in multiple geographic regions and industries. As the world’s leading industrial education and technical training institution, Festo Didactic is helping users build Industry 4.0 talent pools and train engineers to master the new skills needed for future factories.
Ted Rozier, project development manager for Festo Didactic America, said that there may be nearly 300,000 U.S. manufacturing jobs that could not find a suitable candidate because of a lack of qualified candidates. And this figure is expected to continue to grow.
“It is very important for students to become familiar with the complete combination of automation hardware and software.†said Rozier. They need to understand the integration process of robots and PLCs and become familiar with how the Internet of Things improves the entire process. This is a common practice in Europe and we hope to increase The popularity of such training in North America.
Rozier emphasized the importance of multidisciplinary learning, especially the importance of mechatronics. "In order to bring dynamism 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 understanding, but also from management to the workshop. Layers, down from the IT layer down to sensors and other aspects affect the automated manufacturing process and help robots make decision-making talent. This is an important skill development."
Learning factory module
Festo Didactic provides "learning factory modules" for industrial training in practical mechatronics, control technology and automation technology. The system starts with a module: single project workstation I4.0, which is used to train the basic principles of control technology. You can then add several modules to create a complete learning network - entity (CP) facility, which includes a realistic industrial pallet circulation system and an autonomous mobile robot to connect to a different workstation.
The system is modular, so you can add, delete, and move workstations based on changes in learning needs. 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 smart process data modules, companies Resource Planning (ERP) systems, Manufacturing Execution Systems (MES) and rapid prototyping work together.
Some community colleges in manufacturing and STEM tracks are using Festo's CP factory module to train students so that they can immediately start working after graduation. The York Institute of Technology in Rock Hill, South Carolina, USA, has a room dedicated to the CP factory, with approximately six modules. At the Mercedes-Benz plant in Vance, Alabama, Festo is learning to use equipment to increase workers' knowledge. We must prepare ourselves for new ways of interacting people, machines and data in a highly connected world. Now you can take steps to prepare for Industry 4.0 and Smart Factory.
Mobile Robots with Integrated IIoT Platform <br> 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 versatile. To some extent, it can be seen as an unmanned self-driving vehicle (UAV), or even a ground drone. The shape of a robot is similar to a snake, which makes it particularly interesting.
The "snake shaped robot" has been around for about 10 years and was originally used for search and rescue operations in complex terrain environments, which may include muddy environments, earthquakes or mine collapse sites. Sarcos Robotics recently announced its Guardian S robot mobile IoT platform, which is a smart, remotely operated robot designed for use in unpredictable and unstructured environments. The cost of a basic device is approximately $60,000, or a service contract can be signed for $2,000 per month.
The robot integrates the Microsoft Azure cloud computing platform and 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.
"In an open environment, drones have done an incredible job of collecting useful data, but we have found that there are higher demands for data collection in certain application environments, such as closed or closed. Space, or the need to collect data for hours, not minutes, or the opportunity for data to only collect sensors near or in contact with the surface, says Ben Wolff, Chairman and CEO of Sarcos Robotics Say.
The Guardian S robot is like a multipurpose, wirelessly controlled, driverless ground vehicle that can carry multiple sensor payloads as a mobile IoT platform. This 13.5-pound robot can remotely operate and span challenging terrain including stairs, culverts, pipes, 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 of the various industrial machines deployed and for the maintenance needed to predict. Robots equipped with IoT platforms improve cloud efficiency by using Azure cloud computing capabilities and analytics to collect and analyze data related to the robot's surroundings.
(Author: Tanya M. Anandan)
Restraints: High cost of battery-powered portable power stations
Portable power stations include components such as high-efficiency Solar Panels, pulse width modulation (PWM) charge controllers, inverters, lithium-ion NMC (nickel, manganese, and cobalt), and sealed lead acid, which contain silica. As the capacity requirement increases, the battery size and the size of solar panels also increase, leading to higher costs of portable power stations. For instance, key companies such as Goal Zero and Jackery are offering solar panels as separate accessories, which further increases the cost of battery-powered portable power stations. Alternatively, natural gas or fossil fuel generators are comparatively low in cost because of a wide variety of fuel types, such as diesel, gasoline, and natural gas, coupled with reduced fuel prices. Fuel prices are low because of the reduced oil & gas prices globally. In addition, a few fossil fuel-powered portable generators can even run on more than one type of fuel-a combination of gasoline and propane. Natural gas, fossil fuel diesel-based generators or small engines are preferred conventionally over battery-powered portable power stations as these are widely available and easily accessible at any place. Portable generators are relatively inexpensive because of their simpler construction and technological maturity compared with battery-powered portable power stations. These factors may restrain the growth of the portable power station market due to the availability of cheap generators.
Solar Energy Storage,Solar Lithium Battery,Solar Panel Battery Storage,Solar Power Batteries
Shenzhen Zhifu New Energy Co., Ltd. , https://www.sunbeambattery.com