From HVAC units to complex industrial automation applications, embedded systems are ubiquitous; acting as a programmable operating system that specialize in tasks such as monitoring or controlling of the systems. They are designed to maximize performance, improve power efficiency and control processes while operating in demanding environments.
Historically, prior to the application of embedded systems for industrial machines, manual intervention by the operators was required to monitor and control the machines. The status quo posed issues such as vendor specific components, network infrastructure incompatibility, costly and time-consuming integration with existing monitoring and control systems, which did not offer flexibility to support a big industrial setup.
The subsequently introduced and widely adopted PLC and SCADA based systems operated by processing the machine/device/plant data locally. Operators used to record the daily production using production line counters, generate paper-based reports or manually enter machine data on computers. The end-result of these human errors was data discrepancy leading to production loss, increased manufacturing time, effort and costs.
The two primary uses cases of embedded systems are improved machine monitoring and machine control.
Industrial automation systems leverage embedded software development capabilities to monitor the system’s condition in real-time through controlled monitoring of variables like power, flow rate, vibration, pressure, temperature, and more. The monitoring devices such as sensors and probes communicate with each other and/or the client-server systems located in the internet or cloud via the industry communication protocols such as MTConnect, HART, EtherNet/IP etc.
Aggregated data from the disparate data sources is then stored in the cloud or a centralised database for real-time analysis to provide actionable insights through dashboards, reports and notifications. It is a proactive approach to maintaining plant uptime/reliability; reduce production losses and maintenance costs. Industrial embedded systems can perform machine monitoring to help improve productivity, optimize equipment capabilities and measure performance.
Using embedded system engineering services in various industrial equipment to perform specific range of tasks such as controlling assembly line speeds, fluid flow rates in a CNC machine, controlling robotic machinery etc. changed the industrial automation landscape. Communicating at the I/O level via PLCs, these systems easily integrate with the existing machine controls, leveraging automation software along with proprietary NC and CNC functionality. Industrial OEMs and manufacturing plants, can hence benefit from reduced maintenance costs, achieve a centralised and unified control architecture and optimize their performance capabilities and overall product quality.
Industrial OEMs and plant owners vision of Industry 4.0 and IIoT is total and complete automation of the industrial network through intelligent machines and digital systems. The new communication and information techniques mandate:
Localization and networking of all systems using energy-efficient systems that transfer only the required information
Strong security measures for secure data transfer
Our embedded engineering services including but not limited to system/product design and wireless SoC based product development (firmware/stack/hardware), IoT allows us to provide complete end-to-end solutions for the OEMs, process and factory-manufacturing units to address the above-mentioned embedded engineering problems.
Faced with the wide range of embedded system applications, multiple opportunities and challenges, they can realize both economic and performance breakthroughs by opting for Utthunga’s team of highly skilled and embedded professionals certified in product design, firmware architecture, hardware architecture, verification & validation, certifications and a strong partner for PCB fabrication and prototyping.
One of the key enablers for smart manufacturing is the embedded OPC-UA technology that has enabled industrial devices to communicate in a standard, scalable and secure format. Utthunga’s embedded software development services proficiency can help them to achieve platform independence and interoperability to overcome the increased client/server complexity. Our embedded solutions leverage machine learning, AI, and data analytics to help monitor and control the HMIs, vision, PLCs, and motion solutions while offering recommendations for better performance, greater embedded system logic, control, and scalability.
Our embedded stack development services leverages our competencies in embedded technologies to keep pace with the rapidly evolving machine monitoring and machine control requirements and provide embedded industrial automation solutions related to:
Product Design and Development:
End to end product development
Firmware, hardware application development
Electro mechanical product development
Process Automation:
Metering application
Loop powered design and development
IS certification engineering service
Sensor integration and sensor application development
Factory Automation:
Condition monitoring
IoT gateway
Edge computing
Enable legacy machines for IoT
Industrial protocol simulator
Wireless application development
Oil and Gas Services:
Industrial I/O module development
Sensor module development
Level transmitter design and development
Please visit our website or contact us directly to learn more about our embedded software development services and systems expertise.
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.
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.
li>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.
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