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Integration of measurement and control systems: new edge nodes simplify the industrial IOT

recently, no matter where we are, there will be endless discussions about the industrial IOT (iiot). 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 electricity, iiot represents intelligent electricity; Iiot in the oil and gas industry is embodied in the digitalization of well pads. Although different forms of iiot have their specific expressions and processes, the technologies and advantages provided by iiot are roughly the same. Although industry leaders are eager to use iiot, it is difficult to imagine what scenario it will be like to connect 50billion devices by 2020. 1. Experts estimate that half of these new equipment deployed between 2015 and 2025 will come from the industrial sector. This means that engineers and scientists who pass 6000 to 20000 hours of accelerated aging test results will be the drivers of iiot in factories, test laboratories, electricity, refineries and infrastructure

for iiot, engineers can expect to obtain three main benefits:

increase uptime through predictive maintenance

improve performance through edge control

improve product design and manufacturing through real joint data

in order to realize these advantages of iiot, the design team must rely on a number of core technologies. Whether in building 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 is, the more real-time decisions need to be made. For example, in the structural testing of wind turbine blades, the ability to collect a large number of high-resolution simulated waveform data is crucial to understand the behavior characteristics of blades. At the same time, we need to process these data to provide input for the control system, so that the system can drive the blades to ensure that the test is carried out 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 on the edge. 3. In order to maximize performance and reduce unnecessary data transmission, users must delegate decision-making power to edge nodes deployed at or near the device

Figure 1 By 2019, at least 40% of IOT data will be stored, processed, analyzed and responded at the edge

over the years, Ni has invested in two high-quality control and measurement platforms: compactrio and compactdaq. These two platforms are flexible and modular, and have the function of software defining the thickness of tensile and zigzag samples, which should be the thickness of steel. 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 real-time processor and user programmable FPGA, which is especially suitable for high-speed control, while compactdaq provides the best software API Ni daqmx in its class, which is an ideal choice for data acquisition

however, when we begin to implement these systems, new challenges continue to emerge - especially with the increasing physical size of the system and the increasing number of sensors. We still take structural testing as an example. In order to fully understand the performance of wind turbine blades, we need to equip the whole mechanism with sensors to measure strain, pressure, load and torque. These sensors will generate analog signals. In order to obtain the most useful information, we need to make high-speed and high-resolution measurements. For such large-scale applications, we may need to deploy hundreds or even thousands of sensors in the whole system. When collecting all these data, we also need to be able to process these data in real time, so that we can provide output control for all actuators of the control system

some challenges will be encountered when trying to develop such a system:

synchronize thousands of channels and many measurement systems

synchronize the control system so that all operations can be carried out at the right time

synchronize the measurement system and the control system

these challenges will further intensify with the continuous expansion of the system and the increasing number of measurement and control systems applied. Synchronization between measurement systems and between control systems is not a new challenge. Today, we can usually achieve this goal through signal-based methods, in which physical cabling is used to route common time bases or signals to distributed nodes. However, this has limitations in terms of distance, scalability and noise risk. Another option is to use protocols based on general standards such as Ethernet. Ethernet provides a high degree of openness and interoperability, but there is no delay limit or bandwidth guarantee. To solve this challenge, engineers developed a customized version of Ethernet, commonly known as hard real-time Ethernet. Typical examples include EtherCAT, PROFINET, and ethernet/ip. These custom Ethernet versions provide hard real-time performance and state-of-the-art 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 suppliers cannot run on the same network

a new technology to solve this synchronization challenge is being introduced to the market, which is called time sensitive network (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 IEEE 1588 precise time protocol configuration file and provides sub microsecond synchronization through the network. In addition, traffic scheduling and system configuration provide determined 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 function in many industrial applications, such as process and machine control. Low communication delay and minimum jitter are essential to meet the requirements of closed-loop control. 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 significantly reduce the wiring requirements, so that a simpler, cost-effective solution can be achieved without sacrificing reliability

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, and the latest controller of 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 and supports synchronous and deterministic communication

The introduction of

tsn is an important step to solve the synchronization challenge of the whole system. Engineers developing these systems are also concerned about how to reduce the overall system complexity while maintaining or improving reliability. Since measurement and control are usually independent subsystems, tools, programming environment and data acquisition mechanism are independent of each other. PLC and other control systems are usually programmed with IEC language, which can operate single point data. This type of data is not lightweight and reduces the wind resistance at the same time. It is often suitable for control applications, but it is not suitable for extracting information - so we need waveform data. Similarly, the measurement system uses waveform data to provide the required information, but it is not suitable for sending a single point control signal or reacting to a single point control signal definitively

this characteristic of the measurement and control system is very intuitive. In the past few years, the integration progress 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 the control system has some measurement functions. With the release of the latest compactrio controller, we have seen significant progress in this integration. In addition to using real-time processors and FPGAs to realize deterministic control applications, the new controller can also be programmed with an easy-to-use and powerful Ni daqmx driver to realize measurement applications. The calibration step X of Ni daqm metallographic microscope is not only a basic hardware driver, but also provides configuration and fault analysis tools, step-by-step configuration tools, and a powerful and intuitive API, which greatly improves work efficiency and performance. Engineers can use Ni daqmx API to write custom programs, realize powerful timing and synchronization functions, and perform advanced control and monitoring tasks. For users who need to synchronize high channel number systems, develop decision-making based recorders, or automate 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 on the edge side, thus ultimately reducing the cost and complexity of the system


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

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