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Automotive Electronic Systems—a View of the Future [icnweb]



By Ron Wilson, Editor-in-Chief, Altera Corporation

Automotive driver assist systems (ADAS) are the hot topic today in automotive electronics. The systems range from passive safety systems that monitor lane exits, active safety systems like advanced cruise control, to, in the future, situation-aware collision-avoidance systems. The increasing demands ADAS evolution places on data transport and computing are fundamentally changing automotive electronics architectures. And it is becoming clear that these changes foreshadow the future for many other kinds of embedded systems.


Goals and Requirements

Today vehicle-safety electronic systems are isolated functions that control a specific variable in response to a specific set of inputs. An air-bag controller, for example, detonates its explosive charge when an accelerometer output trips a limit comparator. A traction-control system applies a brake to reduce torque on a wheel when the data stream from a shaft encoder indicates sudden acceleration. While these systems make contributions to vehicle safety, they can also act inappropriately because their inputs give them a very narrow view of the world. Hitting a pot-hole or bumping into a car while parking can fire an air bag. A rough road can puzzle traction control.

All that is about to change, according to Steve Ohr, semiconductor research director at Gartner. “Advanced air-bag controllers have multiple sensors that literally vote on whether a crash is happening,” Ohr explained in introduction to his panel at the GlobalPress Summit in Santa Cruz, California, on April 24. “In the near future, the controllers will consult sensors that monitor passengers and cargo to identify how best to deploy the various air bags during a crash.”

At this point, the air-bag controller has crossed a critical threshold: from responding to an input to maintaining—and responding to—a dynamic model of the vehicle. This change, Ohr emphasized, is being echoed in other systems throughout the vehicle, with profound consequences. “We see the same pattern in safety systems such as lane-exit monitors and impending-hazard detectors,” Ohr stated. “Each system is getting more intelligence, moving to sensor integration and then to sensor fusion.”

This evolution is happening in an already astoundingly complex environment. Panelist Frank Schirrmeister, senior director of product marketing at Cadence Design Systems, observed “In 2010, a high-end car could have 750 CPUs, performing 2,000 different functions, and requiring one billion lines of code.” Schirrmeister said that this degree of complexity was forcing developers to adopt hardware-independent platforms such as Automotive Open System Architecture (AUTOSAR), and integrated mechanical-electrical-software development suites. In this fog of complexity, system designers are struggling to cope with a sudden surge of change in the way the systems handle data.


Isolation to Fusion

Hazard-avoidance systems offer a microcosm of this sweeping changes, according to panelist Brian Jentz, automotive business-unit director at Altera Corporation. Today, relatively simple systems like back-up cameras can already have significant processing requirements, Jentz said. “Inexpensive cameras need fish-eye correction to fix the perspective so drivers can interpret the display easily.” These cameras also need compensation to produce useful images in low light, and often they will require automated object recognition. These functions can be done better in the camera, but it’s often cheaper to do them in the central engine control unit (ECU). “Cameras are moving to high-definition,” Jentz continued, “and this can mean megapixels per frame. If you are sending images to the ECU, you may have to compress the data before it leaves the camera.”

Further evolution will complicate the data transport problem further. Hazard detection will move from simply showing an image from a rear-facing camera to modeling the whole dynamic environment surrounding the car. At this point the system must stitch together images from multiple cameras—at least eight for a 360-degree view with range and velocity detection, as shown in Figure 1. A central processor is absolutely necessary, and the ADAS must transport many streams of compressed video to the ECU concurrently.

Figure 1. Placement and use of cameras determines the algorithms required to process the images.

But things get harder still. Video cameras are hampered by darkness and disabled by rain, snow, road spray, and other sorts of optical interference. So designers team the video cameras with directed-beam, millimeter-wave radar to improve reliability in low-visibility conditions. Now the ECU must fuse the video data with the very different radar signal in order to interpret its surroundings. This fusion will probably be done using a system-estimation technique called a Kalman filter.

Kalman and its Discontents

A Kalman filter can take in multiple streams of noisy data from different sorts of sensors and combine them into a single, less-noisy model of the system under observation. It does this, roughly speaking, by maintaining three internal data sets: a current estimate of the state of the system, a “dead reckoning” model—usually based on physics—for predicting the next state of the system, and a table rating the credibility of each input. On each cycle, the Kalman filter assembles the sensor data and uses it to create a provisional estimate of the system state: for example, the locations and velocities of the objects surrounding your car. Simultaneously, the filter creates a second estimate by applying the dead-reckoning model to the previous state: the other cars should have moved to here, here, and here, the pedestrian should have walked that far, and the trees should have stayed where they were. Next, the filter compares the two state estimates, and taking into account the credibility ratings of the inputs, updates the previous state with a new best estimate: here’s where I think everything is really. Finally, the Kalman filter sends the new state estimate to the analysis software so it can be evaluated for potential hazards, and it updates its sensor-credibility table to make note of any questionable inputs.

The good news is that the Kalman filter can assemble a stable and accurate model of the outside world despite intermittent readings, high noise levels, and a mix of very different kinds of sensor data. But there are issues, too. Kalman filters working with high-definition (HD) video inputs can consume huge amounts of computing power, and the analytic routines they enable can take far more, as suggested in Figure 2. “Algorithm development is already ahead of silicon performance,” Jentz noted. “There is basically an unlimited demand for performance.”

Figure 2. Sensor fusion concentrates many heavy algorithms and network terminations on one chip.

There is another issue with important system implications. While Kalman filters are inherently tolerant of noise, they cannot be immune to it. And variations in the latency between the sensors and the ECU—particularly if the variation is large enough for samples to arrive out of order—appear as noise. Such latency variations can cause the filter to reduce its reliance on some sensors, or to ignore altogether information that could have made a vital difference.

This is important because of trends in vehicle network architectures. Purpose-built control networks such as the controller-area network (CAN) or the perhaps-emerging FlexRay network can limit jitter and guarantee delivery of packets carrying some sensor data, although they may lack the bandwidth for even compressed HD video. In principle, system designers could calculate the bandwidth they need for a given maximum jitter, and then provision the system with enough network links to meet the need, even if that resulted in dedicated CAN segments for each camera and radar receiver. But in practice, automotive manufacturers are headed in a different direction: cost control.

“The direction is Ethernet everywhere in the car,” argued panelist Ali Abaye, senior director of product marketing at Broadcom. Abaye said that as the number of sensors increases, cost-averse manufacturers—including the high-end brands—are trying to collapse all their various control, data, and media networks onto a single twisted-pair Ethernet running at 100 Mbits or 1 Gbit.

But a shared network raises the latency issue again. Because Ethernet creates delivery uncertainties, some sort of synchronizing protocol—IEEE 1588, Time-Triggered Protocol (TTP), or Audio Video Bridging (AVB)—would appear necessary. “This is still an active discussion,” Schirrmeister said. “The existing protocols are not yet sufficient for everything these systems need to do.” Abaye, who has 100 Mbit transceivers to sell, is more confident. “Our opinion is that the AVB protocol is sufficient,” he stated.

These debates will have system implications well beyond the cost of cabling. Gigabit Ethernet implies silicon at advanced process nodes, where issues like cost, availability, and soft-error rates become questions. Synchronizing protocols are not exactly light-weight, implying the need for more powerful network adapters. And the need to store and possibly reorder frames of time-stamped data from many sensors could impact memory footprints.


A Multibody Problem

As a final point, when you put radar or scanning lasers into the ADAS architecture, you get a fascinating side-effect. The ADAS on nearby vehicles can now interact with each other. This could lead to sensor interference, or even to an unstable multivehicle system in which two cars hazard-avoid right into each other. This is not a whimsical question: there are hazard-avoidance algorithms that, when used by multiple vehicles in the same traffic stream, are known to lead inevitably to crashes.

“There has already been some research into the behavior of multi-ADAS systems,” Schirrmeister said. “It is an area of continuing interest.”

Such questions will almost certainly involve regulatory agencies in North America and the European Union in the design of ADAS algorithms at some level. Schirrmeister speculated that in developing countries, where cities can spring up and create all-new infrastructure as they go, there may be a move to coordinate ADAS evolution with the development of smart highways.

In any case, it is clear that verification of these systems will involve a significant degree of full-system, and perhaps multisystem, modeling. These will be huge tasks, going well beyond the experience of most system-design teams outside the military-aerospace community.

We have traced the evolution of one automotive system, ADAS, from a set of isolated control loops to a centralized sensor-fusing system. Other systems in the car will follow the same evolutionary path. Then the systems will begin to merge: ADAS, for example, working with the engine-control and traction systems can bypass the driver altogether and maneuver the car away from trouble. The endpoint is an autonomous vehicle—and a network of intelligent control systems of stunning complexity built around a centralized model of the outside world.

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정보 흐름 최적화를 통한 생산성 향상, Syngineer(신지니어)가 구현하는 완벽한 협업



이제 전기 엔지니어들도 기구엔지니어링 CAD 에서 발생한 변경사항을 투명하게 이해할 수 있다.

4차산업혁명 시대에는 여러 엔지니어링 부문이 함께 성장하며 이루어나가야 한다. 그렇다면 현재 이들 간에 존재하는 장벽을 어떻게 극복해야 할까? Syngineer는 전기와 기구엔지니링 설계자들이 ‘서로 대화’를 할 수 있도록 해준다. 이 클라우드 기반의 툴은 기계 및 시스템 개발을 위한 부서간의 엔지니어링 협업을 최적화함과 동시에 좀 더 나은 커뮤니케이션 환경을 제공한다.

위에 제시한 전기설계의 예에서 볼 수 있듯이, Syngineer를 사용하면 프로젝트 진행 상태를 문서화할 수 있다. 모든 정보는 전기 엔지니어링과 기구 엔지니어링 팀 간에 양방향으로 주고받을 수 있다.

위에 제시한 전기설계의 예에서 볼 수 있듯이, Syngineer를 사용하면 프로젝트 진행 상태를 문서화할 수 있다. 모든 정보는 전기 엔지니어링과 기구 엔지니어링 팀 간에 양방향으로 주고받을 수 있다.

많은 설계 엔지니어들이 일상적으로 부딪히는 중요한 도전과제중의 하나는 정보를 획득하고 제공하는 것이다. EPLAN과 그 자회사인 CIDEON(씨데온)이 개발한 혁신적 클라우드 솔루션인 Syngineer는 이와 같은 걸림돌을 극복할 수 있도록 지원한다. 엔지니어링 분야에서의 협업, 특히 기구엔지니어링과 전기엔지니어링, PLC/소프트웨어 사이의 작업 조정을 간소화하고 그 속도를 높인다. Syngineer 시스템을 통해 다양한 엔지니어링 영역에서 서로 정보를 원활하게 교환하며 협업을 증진할 수 있다. 이렇게 함으로써 종종 엔지니어링 마지막 단계에서 수작업이 발생하여 상당한 비용을 초래하게 되는 상황도 훨씬 더 줄일 수 있다.

더 많은 투명성을 제공하는 단순한 시스템

Syngineer는 사용자들을 위한 프로젝트 기반 접근법을 취하고 있으며, 그 적용은 매우 간단하다. Syngineer의 선임 엔지니어링 컨설턴트인 맥스 뤼첼(Max Lützel)은 “Syngineer를 사용하면 MCAD 소프트웨어와 EPLAN Electric P8 간 연결을 손쉽고 빠르게 설정해 사용할 수 있다.”고 밝혔다. 예를들면 “간단한 오리엔테이션만으로도 설계 프로세스상에서 모터 변경이 발생하면, 해당 동료는 그 변경사항을 실시간으로 바로 알 수 있다.”는 것이다.

전기 엔지니어들은 기존의 익숙한 작업 환경에 내비게이터를 추가함으로써 Syngineer를 사용하게 된다. 이 내비게이터를 통해 전기 엔지니어는 타 부서의 기구 엔지니어링의 동료 엔지니어와 연결된다. 전기와 기구, 이 두 영역의 설계 부서가 엔지니어링 정보를 양방향으로 교환할 수 있다. 이와 같은 통합 덕분에 양쪽 엔지니어들 모두 각자에게 친숙한 시스템 내에서 편리하게 작업하면서도 채팅 기능 등을 통해 서로 최신 개발 상황을 조율할 수 있다. 이메일을 통한 변경 사항 통지도 가능하다. 이러한 설정은 알림 관리 시스템을 사용해 손쉽게 맞춤화할 수 있다.

개발 진척 상황을 한눈에 Syngineer에서는 작업 및 요청의 현재 처리 상태를 라벨링할 수 있다. 개발 프로세스 상의 변경 사항과 각각의 진척 상태는 모든 관련 직원이 확인할 수 있다. 나아가 본 시스템은 누가 어떤 변경을 처리했는지에 대한 정보도 제공한다. 그 결과 프로젝트 매니저는 중앙화된 위치에서 프로젝트 상황을 한눈에 볼 수 있고, 사후 고객 요건에 대한 개요를 파악할 수 있다. Syngineer 소프트웨어는 현재 독일어와 영어로 지원되며, 향후 제공 언어가 추가될 예정이다. 세계화 시대에 걸맞게 인터넷 브라우저, 스마트폰, 태블릿, 노트북 등 다양한 경로로 액세스가 가능하다.

이제 전기 엔지니어들도 기구엔지니어링 CAD 에서 발생한 변경사항을 투명하게 이해할 수 있다.

이제 전기 엔지니어들도 기구엔지니어링 CAD 에서 발생한 변경사항을 투명하게 이해할 수 있다.

확장 가능한 시스템

클라우드 기술 기반의 또 다른 중요한 실용적인 이점은 외부 파트너, 고객 및 공급업체도 개발 프로세스에 쉽게 통합될 수 있다는 것입니다. 아울러 클라우드 아키텍처는 유연한 사용자 확장성도 보장한다. 이로써 기업은 필요할 때면 언제든 사용자 수를 조정하며 생산, 커미셔닝, 유지보수 등의 부서에 추가적인 액세스를 제공할 수 있다.

Syngineer는 효율적인 정보 교환을 지원함으로써 시간 절약과 제품 품질 향상을 약속한다. Syngineer를 사용하여 센서/액츄에이터 목록을 자동 생성하면 잠재적인 절감 효과는 더욱 배가된다. 그 결과 기업은 문서화 품질을 높이는 것은 물론, 업무 시간을 30% 단축할 수 있다.


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B&R ACOPOStrak, 휴먼-트랙 콜라보레이션 구축



mapp Trak이 제공하는 안전 기능은 트랙 근처에서 사람이 실제로 작업하는 곳에만 적용된다.

생산 라인에 안전 펜스가 설치되어야 하는 시대는 끝났다. 앞으로 공장에서는 기계와 로봇, 그리고 사람이 함께 협력하여 일하게 될 것이다. 사람과 지능형 수송 시스템이 같은 레벨로 서로 협동하기 위해 ‘휴먼-트랙 콜라보레이션’이라는 개념을 최초로 도입한 제조업체가 바로 B&R이다.

사람은 수많은 생산 라인에서 핵심적인 역할을 한다. 심지어 인더스트리4.0 시대에도 조립과 테스트 작업은 매뉴얼로 하는 것이 가장 좋은 방법중의 하나이다. 사람의 지능으로 빠르게 배울 수 있고 거기에 고성능의 기계가 더해지면 복잡한 타스크도 쉽게 수행할 수 있다. “이것이 ‘휴먼-트랙 콜라보레이션’이 탄생한 배경입니다.” 라고 B&R 메카트로닉 기술 매니저인 Robert Kickinger씨는 말한다.

mapp Trak이 제공하는 안전 기능은 트랙 근처에서 사람이 실제로 작업하는 곳에만 적용된다.

mapp Trak이 제공하는 안전 기능은 트랙 근처에서 사람이 실제로 작업하는 곳에만 적용된다.

사람들이 기계와 협력하여 일할 때에는 안전이 최우선이다. 휴먼-로봇 콜라보레이션(HRC)에 대한 안전 표준과 권고사항이 확립되어 있지만, 휴먼-트랙 콜라보레이션(HTC)은 아직 확립된 사항이 없다. 이것이 놀랄 일은 아니다. 마찬가지로 Kickinger씨는 “우리는 이 분야에서 새로운 영역을 개척하고 있습니다.” 라고 말한다. B&R은 ACOPOStrak 시스템을 적용시킨 매뉴얼 작업 공간에서 안전을 보장하기 위한 방법으로 기술적 측면에서는 휴먼-로봇 콜라보레이션을 위해 정의된 한계치를 사용하고 관련 분야의 C 표준을 적용한다.

5가지 통합된 안전 기능
ACOPOStrak은 휴먼-트랙 콜라보레이션에 있어서 Safe Torque Off (STO), Safely Limited Speed (SLS), Safely Limited Force (SLF), Safe Direction (SDI), Safe Maximum Speed (SMS), 이렇게 5가지 핵심 안전 기능을 갖추고 있다. 최대 안전 응답 시간은 6밀리초로 매우 빠르다. 이 기능들로 ACOPOStrak 셔틀은 사람이 작업하는 공간을 지나갈 때에는 속도와 힘을 제한하고 계속해서 트랙의 나머지 부분을 통과할 때에는 완벽한 성능으로 주행한다.

mapp Trak이 제공하는 안전 기능은 트랙 근처에서 사람이 실제로 작업하는 곳에만 적용된다.

mapp Trak이 제공하는 안전 기능은 트랙 근처에서 사람이 실제로 작업하는 곳에만 적용된다.

B&R 솔루션의 차별화는 좀 더 디테일한 면에서 나타난다. 이에 대해 “안전 속도 및 힘에 대한 제한 값은 고정된 값이 아니라 안전 어플리케이션이 실제 실행될 때 유연하게 계산될 수 있습니다.” 라고 Kickinger씨는 설명한다. 따라서 서로 다른 무게의 셔틀이 위험을 일으키지 않고 각각의 최대 안전 속도로 이동할 수 있다. Kick-inger씨는 “트랙과 같이 작업하는 공간에서 이 정도 수준의 솔루션을 제공하는 것은 우리가 처음입니다.” 라고 말한다.

안전 설정
또한 ACOPOStrak의 안전 기능을 통해 안전 설정 모드를 구현할 수 있다. 설정 모드에서는 속도 및 힘 제한이 전체 트랙에 적용된다. 일단 사람이 안전 구역을 벗어나면 그 제한 사항은 해제된다. “이것은 다른 모든 트랙 시스템에서 사용하는 기능 중 가장 특출난 기능입니다.”라고 Kickinger씨는 강조한다. 다른 시스템에도 속도를 제한할 수 있는 트랙 요소가 있지만 이러한 제한 사항을 조정할 수 있는 시스템은 없다. ACOPOStrak이 아닌 다른 시스템에서는 안전 모드에서 고속 활성화 모드로 전환하는 것은 불가능하다.

ACOPOStrak은 Safe Torque Off (STO), Safely Limited Speed (SLS), Safely Limited Force (SLF), Safe Di-rection (SDI), Safe Maximum Speed (SMS) 등 5가지 핵심 기능을 갖추고 있다.

ACOPOStrak은 Safe Torque Off (STO), Safely Limited Speed (SLS), Safely Limited Force (SLF), Safe Di-rection (SDI), Safe Maximum Speed (SMS) 등 5가지 핵심 기능을 갖추고 있다.

효율성 및 수익성
응답 시간이 매우 짧기 때문에 빠른 조치가 가능하며 매뉴얼 작업공간에서 안전 설정 모드를 사용하더라도 트랙을 둘러싼 기계 설치 공간은 많이 필요하지 않다.

B&R이 소개한 ‘휴먼-트랙 콜라보레이션’은 지능형 ACOPOStrak 수송 시스템의 사용 범위를 확장하는 동시에 시장 출시 기간이 짧아지면서 투자수익률도 올리는 강점이 있다. 복잡한 작업 같은 경우에는 사람에게 맡기는 것이 완전 자동화된 시스템보다 기계를 더 빠르게, 더 저렴하게 사용할 수 있다.

또한 B&R의 안전 솔루션은 작업자가 매뉴얼 작업 공간에서 작업을 진행하는 동안에도 생산을 계속할 수 있기 때문에, 기존의 솔루션보다 설비종합효율(Overall Equipment Effectiveness)를 극대화할 수 있다. B&R은 이 신기술로 제조 라인의 새로운 미래를 개척하고 있다.

B&R은 2017년 SPS IPC Drives 전시회에서 처음으로 지능형 ACOPOStrak 수송 시스템을 선보였다. 초당 4미터 이상의 속도로 제품들은 독립적으로 제어되는 셔틀을 통해 A 가공 작업대에서 B 가공 작업대까지 이동한다. 그 사이에는 제품들을 나누기도 하고 합치기도 하는 다이버터가 있다. 이것은 특히 고객 맞춤화 제품을 생산하는 기계 제조업체들에게 완벽하게 자동화된 제조 시스템을 구축할 수 있는 무한한 가능성을 열어준다. [제공. 비앤드알산업자동화]

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English News

TSN; A Milestone for Industry



The proven communications standard Ethernet has been optimized through the addition of quality-of-service mechanisms. Various new IEEE standards will enable Ethernet to reliably transmit several protocols (including real-time-capable ones) in parallel within predefined maximum time limits. Industrial users and car manufacturer are already getting ready to employ it.

The steel arms move perfectly in step with one another and wave their grippers at the viewer. Not even the minutest delay is noticeable when the two industrial robots perform their graceful machine ballet. This perfect coordination is due to a technology that is currently ushering in a new era of industrial communication: Time-Sensitive Networking (TSN).

For the past 40 years or so, Ethernet has been the undisputed leader when it comes to the transmission of digital data through cables. Siemens has been there from the very beginning and even launched the first Industrial Ethernet network on the market: SINEC H1. As a result, Ethernet was not only used in offices but also, in particular, in industrial manufacturing. However, the standard had a problem from the very start – it could not guarantee that the data packets transmitted by the sender would arrive at the recipient within a certain amount of time. This is an unacceptable situation for industrial controllers – after all, sensor measurements and control signals musn’t take too long to arrive at their destination if a machine is to operate reliably. They need real-time communication within milliseconds – a task for which Ethernet was not originally conceived.

An important development: In the future, everything will build on Ethernet with TSN and can be operated in parallel as required.

An important development: In the future, everything will build on Ethernet with TSN and can be operated in parallel as required. (@siemens)

That’s why users who want real-time communication via Ethernet need to employ technological extensions such as the widespread Profinet standard. In machines, for example, this standard connects sensors, actuators, and drives to the central controller by adding real-time data transfer capabilities to Ethernet, enabling applications up to and including the precise control of servo drives. “However, to do that you generally need special hardware components inside the connected devices,” explains Matthias Gärtner, Head of System Management at the Simatic Controller unit of Siemens. “Moreover, the various real-time Industrial Ethernet solutions cannot be run in parallel on the same Ethernet network.”

TSN will enable all data – including real-time information – to be transmitted through a single network in effect simultaneously.

This problem will soon be a thing of the past, because the Institute of Electrical and Electronics Engineers (IEEE), which is responsible for standardizing various systems, has extended Ethernet by adding the urgently needed mechanisms for real-time communication. These include time-controlled transmission, synchronization, and bandwidth reservation. In this way, the IEEE is improving the quality of service by means of TSN. This will enable Ethernet to supply the same time information to all of the connected devices that support these extended standards. As a result, the entire network will be precisely synchronized. In addition, reservation protocols ensure that the data packets are transmitted from the sender to the destination via all the intervening switches according to a predefined timetable. The TSN standards also take into account the topology of the network in question – i.e. whether the network is arranged in the form of a star, a ring, or a line and the number of switches between the sender and the recipient. Moreover, the standards also include seamless redundancy processes.

Attractive for the entire industry: In addition to real time-capable communication within machines, communication between machines can now also happen in real time, improving throughput in the entire plant system.

Attractive for the entire industry: In addition to real time-capable communication within machines, communication between machines can now also happen in real time, improving throughput in the entire plant system. (@siemens)

A Single Network for All Data
“It’s a historic moment for Ethernet,” says Gärtner. “In the future, it will be possible to use standard hardware components for Profinet and other real-time industrial communication protocols that are based on TSN. This will enable all data – including real-time information – to be transmitted through a single network in effect simultaneously.” Users will automatically benefit from the steadily increasing bandwidth of standard Ethernet, which will be needed more and more as a result of the increasing IP connectivity of the automation systems. It will also make communication more robust because the switching resources in the TSN switches are firmly reserved for the requested real-time communication needs so that information can no longer be lost due to buffer overflows, for example.

In addition to a rising demand for real-time-capable communication in machines via Profinet, there is also a growing demand for deterministic (i.e. predetermined) data exchange between different machines. Examples include cooperative robots that simultaneously work on the same work piece and so need to precisely coordinate their movements with one another. The OPC UA standard with the PubSub (Publish/Subscribe) extension has now established itself in this area. It can also employ Ethernet with TSN as its transmission medium. “I expect Ethernet with TSN to be introduced into the entire industrial manufacturing process,” says Gärtner. “But that’s not all. Car manufacturer also want to use the new standard to transmit the large amounts of data from reversing cameras within vehicles, for example, or to make autonomous driving possible, which won’t be achievable without onboard networks that incorporate quality-of-service mechanisms.” The time for this has now come. The first TSN components are now being launched on the market and Siemens will use actual TSN products to demonstrate deterministic machine-to-machine communication over OPC UA PubSub at Hannover Messe 2018. These products will be purchasable at the end of the year, when Ethernet with TSN will have finally arrived in the modern world of digitalization and “Industrie 4.0”.

more info at




Interview about TSN: “Ideally Prepared for the Future”


Siemens is one of the driving forces behind the development of Ethernet with TSN. Sven Gottwald, Head of System and Vertical Management for Industrial Communication and Identification, explains how the application of the new standard benefits users.

Why is Ethernet with TSN such an important development?

Because it means that we no longer need different Industrial Ethernet solutions for deterministic communication in industry. In the future, they will all run on the widely used Ethernet with TSN and can do so in parallel without restrictions. This includes Profinet, OPC UA PubSub, and all other TCP/IP-based protocols. This convergence is a huge benefit for industrial users, because it enables all types of data to be transmitted through a single physical network in which time-sensitive information always has precedence so that it reliably arrives on time. That’s precisely what companies need in order to fully exploit the potential of digitalization and “Industrie 4.0”.

How does Ethernet with TSN regulate the “timetable” for the data?

The Institute of Electrical and Electronics Engineers (IEEE), which is responsible for the standardization, provides two options for this: It’s either done by a central unit or the connected devices negotiate it among themselves. As a member of the “Labs Network Industrie 4.0” (LNI4.0) testbed, Siemens prefers the second option because the use of a central unit requires extensive reconfiguration every time a change occurs in the network. However, if the system configures itself, it’s easy to integrate new devices. That’s why we talk of “Plug & Work”-capable networks, which greatly benefit our customers.

What is Siemens doing with regard to Ethernet with TSN?

The entire automation industry is working hard on this topic of course. However, Siemens is certainly one of the leading companies in this area. We are actively involved in all of the major standardization bodies, such as the IEEE, the IEC, the OPC-F, and the PI. We also supply the editor for some of the TSN IEEE standards.

The first TSN products will start shipping before the year is out. That’s why our customers are ideally prepared for the digital future with Siemens.


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