Industrial revolution 4.0 has already started to show signs of significant change in various industrial operations. From manufacturing, to automotive, finance and production, every business process is being explored to unveil the potential of automating them.
Industries are thriving hard to stay in tune with the latest technological advancements and be relevant in the digital era. The popularity of software-based automation for industrial units therefore has seen a sharp rise. According to a survey, the industrial control and factory automation market are expected to reach USD 229.3 billion by 2025 from USD 151.8 billion in 2020, at a CAGR of 8.6%.
The I4.0 brings in a lot of improvements in the manufacturing industry. OEMs, in particular, are embracing the rapidly changing technology, and are implementing software that needs timely up-gradation with the inclusion of new features.
Even though the changes work for the betterment of the system, it may also bring unwanted alteration to the existing features. Hence, proper regression testing is required to check if the changes does not alter the intended purpose of the system.
Regression testing uses the requirement specifications as basis for creating test cases and looks for any bugs or fault in the software system. As more and more OEMs and factory process are drifting towards remote functions and software implementation, this testing helps them to improve the overall quality of the software.
Improve efficiency: An OEM with an error-free software ensures precision in its operation. Regression testing constantly checks for deviations in the software each time a modification is made.
Better monitoring and decision-making process: In some cases, especially when dealing with a complex software, OEM tends to lose track of the code modification. Regression testing makes it easier, as it keeps a record of all the changes made. This in turn aids in proper monitoring of the changes and decision-making process related to the deployment of the final software.
Reduces unnecessary manufacturing costs: Regression testing identifies the errors and notifies the OEMs to fix them in the early stages itself. A bug fix in the production/manufacturing stage of the product life cycle will result in huge manufacturing costs. Regression testing ensures the final product will be error-free.
Continuous operation: A crucial aspect in the successful deployment of I4.0 is the assuring the interconnectivity and automation of the devices. Regression testing ensures the bugs are fixed and all the interconnected devices work together seamlessly.
There are different ways regression testing can be carried out. Based on your requirements and the complexity of the software, a proper regression mechanism is chosen.
In industrial automation, devices need to be connected together. Here, with every additional device, the software may need changes in its code or features. The testing here ensures that the introduction of the new device or an upgrade does not alter the functions of an existing setup.
In an OEM unit, regression tests are mostly executed at the design stage to find the immediate bugs and at the production stage to decide whether the quality of the product matched the specification of the customer.
If there needs to be a functional change in any of the devices, corresponding codes need to be changed, Here the regression testing helps in producing the desired outcome.
To keep up with an evolving market, the manufacturing industries and industrial automation in particular are working in an agile environment. The DevOps culture is being widely accepted by the industrial automation companies for on-time and efficient deployment of new software technologies.
The constant upgrades and features introduced by OEMs can change the way the whole system works. This brings in an agile environment where continuous change comes with a high amount of risk.
Risks involve fatal bugs, repeated errors, duplicate entries, etc. These all culminate to either non-delivery of the product or a delay in deployment. Both these cases can be avoided by continuously keeping a check on the code source and its impact, through regression testing.
Benefits of Regression Testing In Agile Environments
OEMs and factory processes are focusing to blend in an agile environment to build a better technology-enabled workspace. This along with the current DevOps culture has helped industrial automation to create a digital identity of its own even in the times of cutthroat competition.
Regression testing helps OEMs to manufacture more reliable products and provide better services. Apart from this obvious benefit, some of the crucial ones are listed below:
Since the software testing and development team can easily identify the bugs, they are motivated to deliver high-end bug-free device
Each case is handled and verified differently, therefore, it ensures a seamless functional process
It ensures the bugs are fixed and the products are ready to be launched in the market.
A bug free software ensures better communication between the interconnected devices in an automation system
The future of industrial automation belongs to agile environment and DevOps. These not only offer a better coping mechanism to the changing scenarios but also are crucial in delivering services with utmost precision. With big data and artificial intelligence seeing new heights, industries are sure to leverage them in software testing to bring the best out of the agile and DevOps culture.Catch up with the most effective testing solutions offered by Utthunga. Contact us to know more.
We have long realized that digitalization is the key to unlocking hidden business opportunities in the industrial sector. Digitization and digitalization becomes more successful when it is integrated across products, services, systems, and solutions. It is therefore essential to establish a seamless interoperability between the components of the enterprise and that of the automation systems. Over the years, industries have faced many challenges in building a unified structure that connects all the components, from factory floor devices to the host applications. We are now closer to the goal of building a unified system that delivers reliable interoperability, thanks to the development of OPC Unified Architecture and its easy implementation in the embedded layer. OPC UA allows you to connect field devices all the way up to the enterprise systems. Embedded OPC UA enables industries to utilize heterogenous data collected from various devices, exchange information with different components, and orchestrate processes across different layers. It also secures data storage and accessibility.
How Embedded OPC UA can Enhance Potential for Interoperability
Embedded OPC UA helps create a singular system (comprised of heterogeneous sub systems) that can work seamlessly with other systems. It presents multiple opportunities for control automation vendors and device vendors to make their products stand out with native open data connectivity that is more secure, easier to integrate in multi-vendor environments, and opens door to new markets due to the widespread use of OPC.
This strong demand for improved access to shop-floor data is driving machine and device vendors to use embedded OPC UA to build products that are interoperable with products (hardware and software) from other manufacturers. OPC UA makes it relatively easy for a multitude of applications to connect with each other. The built-in security enables vendors to provide their applications with the three pillars of secure connectivity: authentication, authorization, and encryption.
Embedded OPC UA SDKs can be ported to many embedded operating systems, including proprietary real-time operating systems, which consume the least memory and CPU resources. It finds application across the horizontal and vertical communication in an enterprise or plant.
Vertical Applications: This includes vertical integration from plant floor devices including the sensors/actuators and controllers in the field to IT systems or the cloud and vice versa.
Horizontal Applications: This includes horizontal integration for controller-to-controller (machine-to-machine M2M) communications.
Both these applications together facilitates the standardized secure communication and is also an enabler for:
Limitations and challenges of industrial interoperability
The biggest challenges of implementing industrial interoperability are:
IIoTresearchers state global standardization as one of the top challenge for industrial interoperability. Industries use devices from various manufacturers and follow unique processes. So, it can be difficult to come up with a one-size-fits-all solution.
Compatibility and connectivity issues
There is a gap in the communication between the existing resources and production process. In most cases, industries have islands of data that are maintained and accessed by different teams. Creating a cohesive network that can build a bridge between these islands of data can be a huge challenge for the developers.
Data security is one of the critical factors that needs to be considered while implementing industrial interoperability. With all the data accessible from one point, the chances of cyber threats and hacks also rises. Therefore, security needs to be strongly considered while building an interoperable system.
It is important to clearly define who will be given access and what they can access. Monitoring accessibility, especially on the production floor can be a huge challenge for all the stakeholders.
In the initial stages, when you are doing a complete overhaul of the existing system, the cost can be a huge limiting factor. Many companies worry about the returns on investment and hesitate to invest in OPC UA solutions. Therefore, it is important to work with experts who come with domain expertise and in-depth experience in OPC UA, so you get full value for your time spent and investment made.
Utthunga has over 12 years of experience in OPC UA server and client development. Our range of OPC UA services includes integration of OP UA in field devices and controllers, OPC UA in Edge devices, OPC UA to database integration, and several other use cases. We also offer OPC UA security consultation services. Contact Utthunga for more details on how you can implement interoperability in your company.
Role of Artificial Intelligence in Industrial Automation
For many people, Artificial Intelligence(AI) means robots performing complex human tasks in sci-fi movies. Actually it is partially true. Whatever AI offers to the world is allowing the industrial machines to carry out super intelligent tasks. As the global industries and decision makers are facing new challenges, there is an urgent requirement to propel manufacturing by using the most advanced technologies. Industries need to restructure and revamp their control systems and other industrial assets (software or hardware) in order to keep pace with the unprecedented speed of change. Artificial Intelligence or AI could potentially help meet these goals. AI applications are already becoming pervasive in industries like banking, gaming, retail, entertainment and more. The fourth industrial revolution is driven by new ways of automating the industrial tasks with smarter sensors, controllers, IO modules, PLCs, gateways, enterprise systems, etc. and restructuring the ways humans and machines interact to create a stronger digital ecosystem.
Machine Learning- The Driving Force of Industrial Automation
With the growing changes in the customer behavior in regard to product quality and customization, it is difficult for the businesses to make changes in their system. That is where Machine Learning (ML) benefits the industries. ML is a subset of AI and empowers the computers to learn automatically from the data inputs and applies that information without any human intervention. ML aids in optimizing the production and supply chain efficiency, fraud detection, risk analysis and risk mitigation, portfolio management, GPS based predictions, targeted marketing campaigns, to name a few.
Machine Learning algorithms are categorized as:
Supervised Machine Learning
This model needs to have a dataset with some observations and labels of the observations that can be used to predict the future events.
Unsupervised Machine Learning
This model needs to have a dataset with some observations without the need of labels of the observations. It does not predict the right output but explores the data and draws inferences from the data sets.
Semi-supervised Machine Learning
This model is positioned between the supervised and unsupervised Machine Learning families. It uses both labeled as well as unlabeled data.
Industries generate tons of valuable data in a single day. With the right industrial AI models, all the raw data can be turned in to useful insights that can lead the designers or engineers in to discovering new ways to improve and update according to the latest technologies.
Improve Product and Service Quality through computer vision
Computer vision tends to replicate the functionalities of human vision and extract important information from the images and videos. Computer vision operates on three main elements that include visual data, high-processing computers and smart algorithms. From the industrial automation perspective, this contributes to the overall increase in production, efficiency, plant safety and security.
Enhance Manufacturing techniques and handle conceptual data with Data-driven Deep Learning and Cognitive Computing
Deep Learning uses ML techniques based on artificial neural networks and is capable of extracting high-level insights from the raw data inputs. Cognitive computing is attentive on comprehending and reasoning at an advanced level, and is capable of handling even symbolic or conceptual data.
Boost Productivity and Safety with Collaboration Robots (Cobots) and Digital Twins
Cobots play a significant role in industries or laboratories. These autonomous systems intend to work alongside humans to pick, place, inject, analyze and pack items. They can also keep track of motion and avoid accidents or errors. Digital Twins can decrease the downtime and cost to set up such robotic systems.
Aid in Decision making with Reinforcement learning and Big Data Analytics
Reinforcement learning is a cutting-edge ML technique that attempts to train the ML models for advanced decision making. The ML model uses trial and error to find the appropriate solution to any complex problem. This technique is widely used in games but it can also shape other industries. Big Data Analytics enables to discover valuable patterns, trends, correlations and preferences for industries to take better decisions.
Making Machine Learning accessible to the end-user with AI-enabled chips
The cloud servers hold most of the computational, storage and networking capabilities. Cloud-based services are great for those who have access to reliable connection and high-speed internet but they are unattainable for those in remote areas. AI-enabled chips can provide access to intelligence without cloud-based services and benefit the industries, especially the ones operating in the remote areas.
Analyze and Predict Future Trends by Deep Learning Platforms
Deep Learning models use unstructured data sets to predict the future trends. Deep learning is crucial for image and speech recognition and depends on three different factors including intelligent algorithms, tons of data and Graphics Processing Unit (GPU) to accelerate learning.
Now AI is playing an increasingly bigger role in our lives. It appears in everything from manufacturing, retail, education and scientific research to banking, criminal justice, hiring and entertainment, to name a few. However, the more we trust this new technology to take important decisions, the higher is the chance for large-scale errors. To prevent such errors, we must understand how and why AI reaches certain conclusions. The two terms which come up in the mind while thinking about improving AI are:
Explainable AI– It comprises of techniques that allow systems to explain their decision making and also offer insight in to the weak and strong parts of their thinking. It will enable us to know how much we can rely on AI results and how to make improvements.
Auditable AI– It takes the help of third parties to test the thinking of the AI system by giving varied queries and measuring the results to find flawed thinking or errors.
Future Trends of Industrial Automation
Further Expansion of IIoT with Predictive Analytics
Predictive maintenance programs are used to track equipment real-time to enhance responsiveness and decrease unplanned outages, resulting in safer operations, lower expenses and higher customer satisfaction.
Increased Implementation of VR and AR Tools
Augmented Reality (AR) and Virtual Reality (VR) tools offer interactive experiences that are specifically used for personnel training. Historically, the personnel training programs have been one-size-fits-all but with AR and VR tools, the training will be more customized based on the skills of the trainee. These technologies will also enable the personnel to train in non-disruptive and safe environment, especially when the training is on rare operations that may be difficult to understand and experience in real-world.
Growth of Edge Computing
The significant rise in data from devices which operate 24/7 often cause bandwidth issues as well as slow processing times. Edge computing technology shifts the information storage and processing from cloud services or data centers towards the specific location where it is required, which is often the device itself. Edge computing can enable the connected devices to make use of more real-time data for business decisions and process controls. Since more and more IoT devices are being used, edge computing is expected to increase.
Expansion of Smart Robot Usage
With advent of 5G network technology, availability of faster and more reliable internet connectivity along with improved satellite coverage to remote areas, the use of smart robotic applications in industries will expand rapidly.
Some Statistical Information on AI
After being a distant aspiration for the industries for many years, we are now more close to adoption and meaningful ROI of AI systems in the industrial landscape. As we see above, the potential advantages of AI adoption into the industrial ecosystem are huge. However, the articulation of the problem statements and the mapping of the right AI tools/technologies to these problem statements is fraught with several challenges. Internal champions (in the plant floor and above) and external technology providers have to collaborate deeply. The promise is there, the execution is the key. Surely, some of these technologies will get even more mature and “easy” to use with time, but choosing to wait and delay implementation will lead to a competitive handicap. Industries should act now, start small, but start now.
Utthunga is a leading engineering and industrial solutions company that can transform your business to leap in to a new world with intelligent, fast, secured and scalable end-to-end intelligent solutions. We understand the industrial domain very well, and are well positioned to leverage the new technologies to deliver the best in class solutions to our customers.For more information on how to build an automated system with Artificial Intelligence and offer the best-fit solution for your industry requirements, contact Utthunga.
Manufacturing industries have evolved drastically over the last decade or so. With the advancements in technologies, manufacturers are aggressively adopting IT/OT convergence tools. Embedded systems, be in in the form of a small smart sensor, or a PLC/DCS, HMI, or an edge device are staring to play a more prominent role. Located in the lower tier of the IIoT hierarchy, these embedded systems are also connected to the cloud either directly or through gateways. The number of continuously connected nodes is increasing. While this trend is proving to be beneficial for the overall manufacturing processes, the dependence on the internet makes the embedded systems highly vulnerable to cybersecurity risks.
This is precisely why cybersecurity threats need to be taken seriously. As a result, OEMs of such hardware systems are looking for secure connectivity mechanisms through innovative security and networking technologies.
In this article, we look into why cybersecurity is an essential element of embedded systems in the IIoT landscape. We shall also discuss the ways to protect it against cyberattacks.
Every time you add a hardware device into your network, you are directly inviting the cyber attackers. These cyberattacks can hamper your company’s productivity and also in the long run, tarnish your company’s image on the global platform.
The meshed network of embedded devices with a cloud backend opens the gate for several cyber threats. Worms can enter the network using a compromised small digital sensor and disrupt the complete production. Several possible scenarios exist. Inadvertent plugging of an infected USB into a system is far too common. You get the drift here. The potential of loss (money and life also) is significant, and cannot be underestimated. Imagine if a cyber attacker hacks one of the embedded devices, say a PLC in an assembly line and changes the critical parameters outside the safe band. Once an attacker gets in the embedded network layer and manages to alter even one device, a chain reaction gets triggered where every interconnected device faces the aftermath of such an attack. This may also result in a fatal shutdown, some severe data breach, or potential loss of life.
A strong security blanket around the embedded nodes is required to keep the sensitive data secured while also allowing process to run seamlessly.
To prevent such cyber-attacks and extend help to the manufacturing industries or any industry that implements IIoT, the industrial internet consortium (IIC) lays down a few guidelines for best cybersecurity practices. Implementing these, you can create secure protective layers around your IIoT ecosystem and prevent attacks on embedded systems. Open Web Application Security Project (OWASP), a nonprofit foundation, also gives guidelines that aim to prevent cyberattacks on your endpoint systems.
Here are some of the best practices you can implement in your IIoT ecosystem to protect your embed systems and ensure a productive Industrie 4.0 journey:
Authentication & Authorization
Authentication can be achieved by having an endpoint identity. A mandatory public critical Infrastructure (PKI) is required for authentication for all levels of security. This ensures each of your integrated hardware such as sensors, actuators etc., are configured only by authorized personnel with recognized level of clearance to prevent from tampering with the settings which may cause
irreparable damages to your IIoT framework. Having authorized security keys or a layer of network security based on internet security protocol prevents attackers from using the information from IT to manipulate the OT systems.
Endpoint security and Trustworthiness
Endpoint nodes like sensors, control systems, or any other embedded field system have the potential to attract cyber attackers. Therefore for endpoint nodes, you need to provide a “root of trust” which forms the foundation for endpoint security. Similarly, embedded systems having debug ports for configuring and testing the devices are also an easy gateway for cyberattacks. To prevent this, you can lockout debugging ports and provide restrictions or other credentials to ensure authorized access to those connection ports. You have to implement robust algorithms to ensure an end-to-end encryption of data to avoid data breaches.
Confidentiality & Integrity
To maintain the confidentiality and integrity of the endpoint system trusted platform module, TPM must be implemented. It includes symmetric and asymmetric keys and functions that ensure secure and seamless communication between devices. It ensures that the sensitive data are confidential and you are protected from attacks. The convergence or integration of hardware and software are covered by a TPM.
Availability & Non-repudiation
One of the crucial aspects in maintaining cybersecurity is to choose a reliable commercial security platform that suits your embedded system requirements. Each of these platforms have their own advantages. Some require low-level hardware implementation while others provide embedded visualization that accelerates the embedded device security. There is no one-size fits for all concept when dealing with your IIoT security.
The recent embedded technology advancements has indeed shaped the world of manufactures for the better. However, it also brings in threats that can prove fatal if you do not take proper care. Lack of security measures leads to unwanted results like declined reliability, increased liability and customer dissatisfaction due to the low quality of services.
To help OEMs, discrete and process manufacturers, the security experts at Utthunga have implemented various security measures to ensure cybersecurity for your embedded systems in the IIoT space. Leverage the cybersecurity services from Utthunga and be assured of adequate security that drives quality services and customer satisfaction.
Industrial automation systems use Ethernet application layers technologies like EtherNet/IPTM and Profinet which often have issues related to bandwidth and payload. To overcome these shortcomings, Beckhoff Automation, a German automation company came forward with a Fieldbus system called the Fast Light Bus. This eventually evolved into EtherCAT (Ethernet for Control Automation Technology), which was launched in 2003 by the same company.
EtherCAT works on the basic principle of pass-through reading and combines the benefits of “on the fly processing” and its well-built infrastructure, some of which includes better efficiency and higher speed.
Utthunga’s industry protocol implementation experts understand this new concept of EtherCAT. We integrate the best EtherCAT practices that enhance your hardware and software communication. This in turn, enhances the overall productivity of the automation system.
Beckhoff promoted EtherCAT protocol through the EtherCAT Technology Group or the ETG. It works along with the International Electrotechnical Commission (IEC), which has led to the standardization of EtherCAT over the years.
The EtherCAT standard protocol IEC/PAS6246, which was introduced in 2003, has since then been standardized by IEC to became IEC 61158. One of the main features of EtherCAT is its versatility to work within most of the industrial plant setups. This flexibility allows it to be the fastest Industrial Ethernet technology suitable for both hard and soft real-time requirements in automation technology, in test and measurement and many other applications.
Another feature of EtherCAT is that the EtherCAT master mostly supports various slaves with and without an application controller. This makes the implementation seamless and succesful.
EtherCAT switches are another unique feature of the EtherCAT. Here the switching portfolio refers to all the managed, unmanaged, and configurable switch product lines. An example of the EtherCAT switch is the Fast Track Switching that offers perfect decision-making capabilities even in a mixed communication network under any circumstances. Most of these switches are quite economical to be used in the switch cabinets. They have robust metal housing and support via either VLAN support, IGMP snooping, or other SNMP management features.
To get the best out of the EtherCAT implementation in your industrial plants, you need to have a robust implementation strategy in place. For this matter, we have jotted down the strategies you can use to implement EtherCAT in the following lines:
The EtherCAT infrastructure is quite powerful as it includes various safety and communication protocols and includes multiple profile devices. If we go deep into the architecture, we see that the EtherCAT master uses a standard Ethernet port and network configuration information. The data can be easily fetched from the EtherCAT Network Information file (ENI). The EtherCAT Slave Information files (ESI) that are unique for each device and are provided by the vendor form the basis of these ENI.
If you are working on a load-dependent servo task, the location (Master or Servo Drive) plays a key role in selecting an EtherCAT mode of operation.
EtherCAT Slave & EtherCAT Master devices
EtherCAT slave devices are connected to the master over the Ethernet. Various topologies of EtherCAT can be implemented to connect slaves to the Ethernet Configuration tool. The Ethernet configuration tool is connected to the EtherCAT master via the above mentioned EtherCAT network information file. This configuration tool plays a pivotal role in implementing EtherCAT and connecting the slaves to the master in the right way. The tool generated a network description known as the EtherCAT Network Information file based on the EtherCAT Slave Information files and/or the online information at the EEPROM of the slave devices, including their object dictionaries.
Several EtherCAT slave devices work synchronously with the EtherCAT master devices through various tuning methods. Here the tasks such as setting outputs, reading inputs, copying memory, etc. can be considered, wherein the synchronization between the logical level of the devices plays an imperative role. In order to implement an EtherCAT slave with a master, all you need is an EtherCAT master that works on the lines of a standard Network Interface Controller (NIC, 100 MBit/s Full duplex) protocol. To seamlessly integrate master and slave, a master software with actual run time drives the slaves.
EtherCAT Operation Modes and Network Topology
One of the challenging parts of implementing EtherCAT to your control system is choosing the right operation modes. The reason being, each mode demands differently from the operating system and master. The possible cases of dynamic changes of loads or custom control loop algorithms and the demands of an “on the fly processing system” needs to consider while choosing the operating mode. Here we are going to give you a brief idea of three of the important operating modes that are: CAN over EtherCAT, File over EtherCAT, EtherNet over EtherCAT.
CAN over EtherCAT (CoE)
One of the most widely used communication protocols, the CANopen, is used in this mode. It defines specific profiles for different devices. This operating mode ensures a higher speed EtherCAT network.
File over EtherCAT (FoE)
This operating mode gives you access to data structure or the information files in the device. This enables the uploading of standardized firmware to devices. This does not depend on whether they support other protocols like TCP/IP.
Ethernet over EtherCAT (EoE)
This operating mode allows communication between Windows client applications with an EtherCAT device server program via Ethernet that uses the EtherCAT network. It the simplest way master and slave connect and reduces the overall implementation time.
EtherCAT is one among the many Ethernet based fieldbus protocols but has garnered significant popularity for industrial automation applications. These are optimized for industrial devices like programmable logic controllers (PLCs), I/O, and sensor-level devices. Implementation of EtherCAT offers higher efficiency of the control system, reduction in error, and connect 65,553 nodes in the system with low latency in each slave node.
Utthunga’s services help you to implement EtherCAT in the best possible ways so your industrial automation systems can enjoy the benefits from its versatility to the fullest.
Industrial revolution 4.0 has already set in, and industrial automation is a profound part of it. One of the crucial aspects of implementing a successful automation ecosystem in any industry is seamless communication between devices. For a long time, the traditional field buses like PROFIBUS, HART, FF, Modbus and a few others have been the been the standard communication solution for field layer connectivity.
However, with the ubiquity of Ethernet in the layers above sensors/PLCs and to take advantage of the IT tools and technologies in the OT layer, Ethernet is increasingly being looked as a communication bus in the field layer also. this has led to the idea of the Ethernet Advanced Physical layer (APL).
Ethernet-APL is a subset of the widely used Ethernet standard and it is describes the physical layer of the Ethernet communication specially designed for industrial engineering services. With high communication speeds over long distances and a single, twisted-pair cable for power and communication signal supply, this layer proves to be a robust solution for a better bandwidth communications link between field-level devices and control systems in process automation applications. In simple terms, Ethernet-APL is the upgraded link between Ethernet communication and instrumentation.
Ever since BASF, a German chemical company and the largest chemical producer in the world successfully tested Ethernet APL for the first time in 2019, many companies have successfully implemented the same in various IIoT networks. In February 2020, ABB’s trials proved that Ethernet APL effectively eliminates gateways and protocol conversions at various industrial network levels.
Ethernet-APL makes infrastructure deployment a seamless process as the devices connected over it share the same advanced physical layer. This also indicates that it enables devices in the industrial network to be connected at any time, irrespective of where they are placed in the factory or processing plant.
There are numerous reasons why industries willing to integrate IIoT must consider Ethernet-APL. We have discussed them in the next sections.
Ethernet-APL enables seamless integration of various processes and creates effective communication between the control and plant field devices for long distances process variables, secondary parameters, and asset health feedback and seamlessly communicating them over long distances.
Some of the major benefits of incorporating Ethernet APL in industrial automation applications are:
Improved Plant Availability
In addition to pure process values, modern field devices provide valuable additional data. With Ethernet-APL, plant operators can make the most of the devices in real-time, centrally monitor their components’ status, and identify maintenance requirements early on. This avoids unplanned downtime and increases plant availability significantly.
Cost-Effective Plant Optimization
Ethernet-APL supports the trunk-and-spur technology established in the process industry and is applicable to any industrial Ethernet protocol such as EtherNet/IP,HART-IP, and PROFINET. This simplifies integration for planners, plant designers, and plant operators since existing installations and infrastructures can still be used and investments are protected.
Adds Flexibility to the Plant
IEEE and IEC standards layout communication protocol, testing, and certification of products to implement Ethernet-APL into any plant automated systems in any part of the world. This way, in an industrial environment, devices from different manufacturers, irrespective of their state of origin, can have interoperable communication within the working ecosystem.
Coherent Communication at all levels
Ethernet-APL allows a common communication infrastructure for all levels of process management. This is because field devices can be easily connected to the higher-level system. The high transfer speed of 10Mbit/s and the full-duplex infrastructure make it suitable for data transmission over a length of approximately 1000 m.
APL – For IIoT Applications
The Industrial Internet of Things is undoubtedly an integral part of the industrial automation workspace. Therefore, the high-speed, industrial Ethernet-based Ethernet-APL is touted as the future of industrial communication systems. Many of the leading communication protocol associations like the OPCFoundation, ODVA, PROFIBUS, and PROFINET International are in the process of supporting APL, which makes it compatible with any existing processing system.
It supports 2-WISE (2-wire intrinsically safe Ethernet) and therefore eliminates the need for numerous calculations, which makes it simpler to verify the intrinsic safety of devices within the Ethernet-APL automation network.
Ethernet-APL comes as a blessing for the manufacturing and process industry in particular, as they lacked a standard network capable of high-speed transfer of data within field devices irrespective of their implementation level in the Industry 4.0 architecture.
How APL is Serving the Special Requirements of Process Industries
Ethernet-APL is specially crafted for process industries. Since these industries involve works at hazardous and explosive areas, deployment of industrial Ethernet seemed like a far thought for quite long. However, with the introduction of an advanced physical layer into the Ethernet, 2-WISE became a reality.
The 2-WISE infrastructure makes it safe to be deployed in such hazardous areas. This improved the overall plant availability and brought remote access to many devices in the process industry 4.0.
Advanced Physical Layer or APL has brought in a new ray of hope for effective adoption and implementation of IIoT in the industries. Utthunga’s innovation-driven team is ready to support you in your APL plans. Get in touch with us and get the best industrial engineering services that elevate the efficiency of your plant and plant assets for increased ROI.