Recently, no matter where we are, discussions about the Industrial Internet of Things (IIoT) will continue to be heard. Moreover, for different industries, this trend is manifested in different aspects. For example, Industry 4.0 is a concept developed specifically for production equipment. In the field of power grids, IIoT is represented as a smart grid; IIoT in the oil and gas industry is reflected in the digitization of wellsites. Although different forms of IIoT have their specific expressions and processes, the technology and advantages provided by IIoT are roughly the same. Although industry leaders are eager to use IIoT, it is difficult to imagine the scenario where 50 billion devices will be connected by 20201. Experts estimate that half of these new networked devices deployed between 2015 and 2025 will come from the industrial sector2. This means that engineers and scientists will be the drivers of IIoT in factories, test laboratories, power grids, refineries, and infrastructure.
For IIoT, engineers can expect three main benefits:• Increase uptime through predictive maintenance
• Improve performance through edge control
• Improve product design and manufacturing through real networked data
In order to realize these advantages of IIoT, the design team must rely on a number of core technologies. Whether you are building online monitoring systems, intelligent manufacturing machines, or testing physical or electromechanical systems, a key commonality is the need for edge intelligence. The more complex the system, the more real-time decisions need to be made. For example, in the structural testing of wind turbine blades, the ability to collect large amounts of high-resolution analog waveform data is critical to understanding the behavior of the blade. At the same time, we need to process these data and provide input to the control system so that the system can drive the blades to ensure that the test is performed under known conditions. Therefore, it is not surprising that experts estimate that at least 40% of IoT data will be stored, processed, analyzed and responded to at the edge3. In order to maximize performance and reduce unnecessary data transmission, users must delegate decision-making power to edge nodes deployed at or near the equipment.
Figure 1. By 2019, at least 40% of IoT data will be stored, processed, analyzed, and responded to at the edge.
Over the years, NI has invested in two high-quality control and measurement platforms: CompactRIO and CompactDAQ. Both platforms are flexible and modular, and have software-defined functions. The built-in I/O interface and C series I/O module provide high-precision I/O and specific measurement signal conditioning, so users can connect any sensor or device through any bus. CompactRIO provides a real-time processor and user-programmable FPGA, which is especially suitable for high-speed control, while CompactDAQ provides the best-in-class software API NI-DAQmx, which is ideal for data acquisition.
However, as we set out to implement these systems, new challenges continued to emerge-especially as the physical size of the system continues to increase and the number of sensors continues to increase. We still take structural testing as an example. In order to fully understand the performance of wind turbine blades, we need to equip the entire mechanism with sensors to measure strain, pressure, load and torque. These sensors all generate analog signals. In order to obtain the most and most useful information, we need to make high-speed, high-resolution measurements. For large-scale applications such as these, we may need to deploy hundreds or even thousands of sensors throughout the system. When collecting all these data, we also need to be able to process the data in real time so that we can provide output control for all actuators of the control system.
There are some challenges when trying to develop such a system:• Synchronize thousands of channels and numerous measurement systems together
• Synchronize the control system so that all operations are carried out at the right time
• Synchronize measurement system and control system
As the system continues to expand and the application of measurement and control systems continues to increase, these challenges are further exacerbated. Synchronization between measurement systems and between control systems is not a new challenge. Today, we can usually achieve this goal through a signal-based approach, where physical wiring is used to route a common time base or signal to distributed nodes. However, this has limitations in terms of distance, scalability, and noise risk. Another option is to use protocols based on common standards such as Ethernet. Ethernet provides a high degree of openness and interoperability, but there are no delay restrictions or bandwidth guarantees. To solve this challenge, engineers developed a customized version of Ethernet, usually called hard real-time Ethernet. Typical examples include EtherCAT, PROFINET and EtherNet/IP. These customized versions of Ethernet provide hard real-time performance and best-in-class low latency and control. However, each version needs to modify the hardware and software of the network infrastructure, which not only increases the cost, but also means that different devices from different vendors cannot run on the same network.
A new technology to solve this synchronization challenge is currently being introduced to the market. This technology is called Time Sensitive Networking (TSN). TSN is an updated version of standard Ethernet, which not only has openness and interoperability, but also provides the same low latency and bandwidth guarantee as hard real-time Ethernet. Specifically, TSN provides three key components: time-based synchronization, traffic scheduling, and system configuration. The synchronization function is based on the IEEE 1588 precise time protocol profile and provides sub-microsecond synchronization through the network. In addition, traffic scheduling and system configuration provide deterministic data communication, so users can schedule and prioritize time-sensitive data (such as control signals) on the network.
An important feature of TSN is the integration of time-sensitive traffic and other Ethernet traffic. Since TSN is a feature of the Ethernet standard, the two new functions of time synchronization and deterministic communication can support all Ethernet communication networks. This means that a single port on the measurement or control system can perform deterministic communication, while remotely updating the user interface terminal and supporting file transfer. TSN is a new feature for many industrial applications, such as process and machine control, where low communication delay and minimum jitter are essential to meet closed-loop control requirements. Time-based Ethernet synchronization can also eliminate the wiring required for signal-based synchronization. Compared with traditional monitoring applications and physical system testing (such as structural testing), it can greatly reduce wiring requirements, thereby enabling reliability without sacrificing reliability Achieve simpler, cost-effective solutions.
Figure 2. Time-sensitive network is an updated version of standard Ethernet, including time-based synchronization, traffic scheduling, and system configuration.
NI products are also increasing support for TSN. The latest controller of the CompactRIO platform is a typical product. Users can add these new controllers to the TSN network and support data synchronization and deterministic communication, making them ideal IIoT edge nodes.
Figure 3. The latest CompactRIO controller supports TSN, which supports synchronous and deterministic communication.
The introduction of TSN is an important step to solve the synchronization challenge of the entire system. The engineers who develop these systems are also focusing on how to reduce the overall system complexity while maintaining or improving reliability. Since measurement and control are usually independent subsystems, tools, programming environments, and data acquisition mechanisms are independent of each other. PLC and other control systems usually use IEC 61131-3 language programming, which can operate on single-point data. This type of data is great for control applications, but not for extracting information-so we need waveform data. Similarly, measurement systems use waveform data to provide the required information, but they are not suitable for sending single-point control signals or reacting to single-point control signals with certainty.
This feature of the measurement and control system is very intuitive and clear. In the past few years, the progress of the integration of measurement and control systems has been very slow. Each system has added new functions so that more measurement systems can have some control functions, or control systems have some measurement functions. With the release of the latest CompactRIO controllers, we have seen significant progress in this integration. In addition to using real-time processors and FPGAs to implement deterministic control applications, the new controller can also be programmed with the easy-to-use and powerful NI-DAQmx driver to implement measurement applications. NI-DAQmx is not only a basic hardware driver, it not only provides configuration and fault analysis tools, step-by-step configuration tools, but also provides powerful and intuitive APIs, which greatly improves work efficiency and performance. Engineers can use NI-DAQmx API to write custom programs, implement powerful timing and synchronization functions, and perform advanced control and monitoring tasks. For users who need to synchronize high-channel count systems, develop decision-based recorders or automated laboratory experiments, hundreds of examples, vibrant communities, and first-class local support can help them quickly transition from concept to deployment. Through this integration, they can use the same hardware and a single software tool chain to directly collect, process, record and respond to the input data at the edge, thereby ultimately reducing system cost and complexity.
references• 1Cisco, The Internet of Things: How the Next Evolution of the Internet Is Changing Everything, 2011
• 2IHS Markit, IoT Trend Watch 2017, 2017
• 3IDC FutureScape: Worldwide Internet of Things 2017 PredicTIons
Mini Pc V310 Series,Mini Fan Pc,Ryzen Nuc,Amd Ryzen Mini Pc
Guangdong Elieken Electronic Technology Co.,Ltd. , https://www.elieken.com