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A primer on the technologies enabling Edge Analytics

A primer on the technologies enabling Edge Analytics

by | Oct 13, 2021 | Blogs

Edge analytics is a sophisticated data analysis technique that allows users to access real-time processing and extraction of unstructured data collected and stored on the network’s edge devices. Edge analytics enables the automated analytical processing of produced data in real-time.

The substantial rise in edge analytics applications is due to the widespread use of the internet of things (IoT), widely acknowledged by industries as the most important tech trend due to the broad range of IoT services and domain capabilities.

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How does edge analytics work?

When used as a data-driven approach to extracting tangible and measurable metrics from the IoT, edge analytics enables businesses to obtain data faster by deploying advanced analytics and machine learning at the point of data collection via connected devices and real-time intelligence.

In terms of the industrial IoT industry, it is expected that industries using edge computing will grow further owing to the edge analytics advantages of dispersed aggregation and offering shop-floor data gathering across all industrial sectors.

The idea of edge analytics allows for developing an optimum model for managing data transmission from the edge and ‘smart’ data storage in data centers. Advanced analytics techniques, independent of their application area, operate behind the scenes to anticipate system component failure, keep devices operational, and ensure a continuous workflow.

Overall, edge analytics skills are actively used to develop smart machines that perform self-responsive activities and anticipate consequences.

Edge AI video-based analytics have found widespread use in a variety of areas, most notably education. Indeed, it can revolutionize the teaching-learning process by providing innovative methods for delivering instructional films in novel ways. Sony, for example, demonstrated its AI edge analytics-based solution for producing video content: its primary unit, in conjunction with different licensed appliances, demonstrates how video streaming may be elevated to a new level.

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Why do edge analytics matter in 2021?

Industry 4.0 is now facing significant obstacles that are impeding its development. Computing at the edge is the answer to many of these problems. Here are some of the most significant benefits that the edge offers to Industry 4.0.

Increased interoperability

IoT systems may be a hurdle to smart manufacturing’s growth. However, an IoT network is just as good as its interoperability.Concerns about interoperability are among the most significant obstacles to its adoption since there is no standard protocol. By relocating computer operations to the edge, part of the requirement for a universal standard is eliminated. When devices can transform signals on their own, they will operate with a wider range of systems.

The edge also acts as a link between information and operational technologies.

Reduced latency

When sending data to a distant cloud data center for analysis, the latency is lengthy and unpredictable. For time-sensitive use-cases, the chance to act on the data may be lost.

Edge computing provides predictable and ultra-low latency, making it suitable for time-critical scenarios such as any use-case where the process is mission-critical or in motion. Delays in moving cars, equipment, components, people, or fluids may result in lost opportunities, excessive expenses, or security concerns.

Analytics-enabled technologies

Companies decide to include edge computing technology in their network architecture due to the number of benefits it offers. There is something for everyone, from real-time AI data analysis and improved application performance to significantly reduced operational expenses and planned downtime.

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Technologies enabling Edge Analytics:

Edge Computing with Multiple Access

Multi-access Edge Computing or MEC is an architecture that allows for computing and storage resources inside a radio access network (RAN). The MEC contributes to increased network efficiency and content delivery to end-users. This device can adjust the load on the radio connection to enhance network efficiency and reduce the need for long-distance backhauling.

Internet of Things

The idea of the Cloud of Things (CoT) is still in its early stages, but it has many potentials. All computing power in a Cloud of Things is obtained from the extreme edge at the end-user.

We all have powerful gadgets in our hands, such as industrial tablets, smartphones and Internet of Things (IoT) devices in our plants. These gadgets are often underused. Although IoT devices currently have limited computing capacity, many modern devices are very powerful.

Using mobile or IoT enabled devices, cloud services may be provided directly at the edge. These devices may be coordinated to provide edge cloud services. For example, a vehicle traveling through traffic may transmit traffic warning alerts to others and re-arrange alternative routes without user involvement.

The idea of the Cloud of Things is akin to that of fog computing. All IoT devices in a CoT form a virtualized cloud infrastructure. All computing in the CoT is done by the IoT devices themselves, which are shared resources.

Fog Computing

Fog computing, fog networking, or simply “fogging” refers to dispersed computer architecture. It brings cloud computing (data centers) to the network’s edge while also locating data, storage, and applications in a logical and efficient location. This location occurs between the cloud and the data source and is often referred to as being “out in the fog.”

Future of edge analytics

Edge computing has gained traction because it provides an efficient answer to growing network issues connected to transferring massive amounts of data that today’s businesses generate and consume. It’s not simply a matter of quantity. It’s also an issue of time; applications rely on processing and responses that are becoming more time-critical.

According to projections, there will be 21.5 billion linked IoT devices globally by 2025. Consider if half of them could do computational work for other devices and services. This massive, linked computer network would be especially useful for smart manufacturing.

Conclusion

Edge analytics is a burgeoning area, with companies in the Industrial Internet of Things (IIOT) industry increasing their investments every year. Leading OEM and suppliers are actively spending in this rapidly growing industry. Edge analytics offers real business benefits by decreasing decision latency, scaling out analytics resources, addressing bandwidth problems, and cutting expenditures in industries such as retail, manufacturing, energy, and logistics.

Utthunga’s expertise in edge analytics and edge computing along with IIoT technologies enables organizations to scale their processing and analytics capabilities exponentially. Contact us for your edge analytics requirements to leverage the real-time business insights from the edge.

A Quick Overview of a Few Industrial Safety Protocols

A Quick Overview of a Few Industrial Safety Protocols

by | Oct 13, 2021 | Blogs

Industrial safety protocols are communication standards used to keep machines, systems, and people safe in manufacturing plants, processing facilities, and other industrial environments. They make sure critical data—like shutdown signals, sensor alerts, or control commands—gets where it needs to go without delay, distortion, or loss.

These protocols catch issues like message corruption, missing data, or duplicated commands. They play a central role in how automated systems stay in sync, especially when multiple devices are working together under strict safety conditions.

Most automation systems involve a mix of control, motion, synchronization, and safety tasks. Industrial safety protocols manage all of that. One example is the Common Industrial Protocol (CIP), which uses an object-based structure to model devices and define how they exchange information. From basic sensors to complex motion systems, protocols like CIP give these components a safe, reliable way to communicate.

Common Industrial Safety Protocols You Should Know

Here’s a breakdown of the protocols most commonly used in industrial safety systems:

• Ethernet

A baseline network technology. Fast, scalable, and widely supported.

• CC-Link Industrial Networks

Known for high-speed data exchange. Common in Asian manufacturing setups.

• HART Protocol

Combines analog and digital signals. Still widely used in process industries.

• Interbus

Real-time communication between sensors and actuators.

• RS-232 and RS-485

Traditional serial communication methods. Still used in legacy systems.

• CIP Safety

Built on the Common Industrial Protocol. Handles safety-specific messaging.

• PROFIsafe

Integrates with PROFINET and PROFIBUS. Frequently used in European automation systems.

• openSAFETY

  • Ethernet-based, open standard. Platform-independent and flexible.

• FSoE (FailSafe over EtherCAT)

EtherCAT’s safety extension. Fast and reliable for high-performance systems.

Each one is used depending on system architecture, safety requirements, and regional preferences.

How Mobile Apps Support Industrial Safety Protocols

Mobile apps are now a core part of safety infrastructure. They give workers and supervisors real-time access to safety data pulled directly from systems running on these protocols. Before mobile tools, much of this was tracked manually, which meant delays, gaps, and avoidable risks.

Now, industrial mobile apps tie directly into protocols through IoT sensors and cloud-based systems. They allow you to:

  • Detect early signs of equipment failure
  • Monitor safety alerts and react faster
  • Track inspections and compliance in real time
  • Store incident data in the cloud for later analysis

These tools help site managers, technicians, and contractors respond faster, keep better records, and reduce the chance of injury or downtime. They’re not just for convenience. They’re now part of how modern safety gets done.

Industrial safety protocols aren’t optional. They’re built into the core of any serious industrial operation. They make sure systems talk to each other clearly, act when needed, and alert humans before something goes wrong.

Add mobile technology into the mix, and you get faster response, better record-keeping, and fewer gaps in communication. If your safety systems aren’t connected and accessible, they’re incomplete.

Need help choosing a protocol or building mobile tools around one? Let’s talk!

Role of Product Engineering Services in Modern Technology Space

Role of Product Engineering Services in Modern Technology Space

by | Sep 20, 2021 | Blogs, Uncategorized

Product engineering services play a central role in turning ideas into real, usable products. From the first rough concept to the final handoff for manufacturing, product engineering covers everything: planning, design, development, testing, and support.

Product engineering service or PES combine hardware, embedded systems, software, and IT to build reliable, efficient, and cost-effective products. Companies often partner with engineering service providers to manage the complexity of this process without pulling focus from their core business.

What is Product Engineering?

Product engineering is the structured process of designing and developing a product from the ground up. It involves defining the idea, shaping its architecture, creating its design, and then moving through development, testing, release, and long-term support.

This work often spans multiple disciplines—mechanical, electronic, embedded, and software engineering—all working together to produce a product that meets technical, functional, and business goals.

Key Phases of Product Engineering

There are seven main stages that define a typical product engineering cycle:

1. Product Ideation

This is where it starts. A product concept is shaped and requirements are defined. Teams look at feasibility—whether the idea is worth building and how it fits into market needs.

2. Architecture

Once the idea is approved, the next step is to break it down into physical and functional blocks. This phase determines what the product will include and how different components will work together.

3. Design

Engineers create models, refine structures, and work through multiple versions until the final design is locked in. User experience, cost constraints, and performance targets all influence the result.

4. Development

Designs are built into actual working systems. This stage includes prototyping, coding, board development, and more. The goal is to create a version that’s fully functional and production-ready.

5. Testing

Rigorous testing ensures the product works as expected. Faults are flagged and resolved. The team validates performance, safety, compatibility, and reliability before anything goes to market.

6. Release

After testing, the product is introduced to the market. Feedback from users is collected to guide updates and fix any missed issues.

7. Product Sustenance and Re-engineering

Support doesn’t end after release. This phase involves updates, maintenance, bug fixes, and, when needed, re-engineering to keep the product relevant. Some companies evolve their products over time, based on new needs and changing tech standards.

Why Product Engineering Services Matter

Product companies face ongoing pressure to deliver reliable products quickly, control costs, and reduce risks. At the same time, they need to improve how they manage their product lines and respond to changing demands.

What often makes the difference isn’t just the idea behind the product—it’s how well that idea is shaped, built, tested, and supported. That’s where Product Engineering Services step in. They allow businesses to focus on strategy and customer needs, while a dedicated team takes care of technical development from start to finish.

The right PES partner brings practical skills across experience design, web and mobile development, cloud systems, DevOps, data handling, and infrastructure. That range of support helps reduce delays, fix issues early, and keep the product aligned with real business goals.

Why Companies Choose Product Engineering Services

Bringing a product to market isn’t just about engineering skill. There’s pressure to move quickly, manage costs, reduce risks, and still hit quality targets. That’s where Product Engineering Services (PES) come in.
A reliable PES partner can help you:

  • Add advanced features and improve functionality
  • Launch products faster without compromising quality
  • Cut costs while keeping engineering standards high
  • Support future updates and maintenance with ease

Companies use PES not just to extend internal teams but to bring in focused expertise at each step of the product cycle.

Utthunga’s Product Engineering Services

At Utthunga, we help product companies design, develop, and sustain high-quality products. Our services span across embedded systems, cloud platforms, software development, and industrial protocol integration. The goal is to provide engineering support that’s technically sound, flexible, and built for long-term reliability.

Here’s what we offer:

Core Engineering Services:

  • Embedded Engineering – hardware, firmware, system design, validation
  • Digital Engineering – cloud, mobile platforms, analytics, IIoT
  • Software Engineering – applications across embedded and enterprise systems
  • Quality Engineering – application testing, device testing, protocol testing, test automation,  testing as a service (Taas), and DevOps
  • Data Connectivity and Integration – OPC solutions, industrial protocols, field device integration

Key Capabilities:

  • Asset and device management tools
  • OPC and industrial data integration
  • Digital engineering, customer-focused digital interfaces
  • Cloud, Edge Computing, Device & Data Analytics
  • IT/OT system integration
  • Engineering of controllers, IO modules, and host devices

We also offer engineering accelerators and frameworks that reduce product development time and help minimize issues after release:

  • DPI (Device Programming Interface)
  • uOPC Suite
  • Protocol Stacks
  • IIoT Accelerators (Javelin and uConnect)
  • Application Test Automation Framework

Need expert support across your product development cycle or to scale up your engineering efforts? Our Product Engineering Services are built to support your goals—connect with us to learn more.

Top 10 Advantages of Industrial Automation

by | Sep 13, 2021 | Blogs

What is Industrial Automation?

Automation helps save a lot of time and effort and at the same time, it helps get a job done more accurately. Industrial automation involves the use of control systems, computers and robots to handle and perform certain operations. A survey by Fortune Business Insights reported that the global industrial automation market will reach 296.70 billion dollars by 2026. In this blog, let’s explore the top 10 advantages of industrial automation. In this blog, by Industrial Automation, we are referring more to the advancements of this century, i.e. after the time computers and electronics started to play a key role in the industrial sector.

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Industrial Automation in Manufacturing

Automation is greatly transforming the manufacturing industry. This revolution is bringing cyber-physical systems into existence. Many manufacturing units are already employing robots and using digital technology to automate processes. A few examples of automation in manufacturing are:

  • Automation of food and beverage packing to reduce chances of human contamination
  • Numerical control in the machine tool industry
  • Use of thermal sensors to monitor high activity areas on the floor
  • Automatic sorting in the production line
  • 3D printing

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Top 10 Advantages of Industrial Automation

  1. Reduces Cost

One of the top advantages of automation is reduction in manufacturing costs. Instead of having a floor full of workers, you can now have a few supervisors and have robots do the job. The initial investment will be a little high, but then the operation costs will reduce, which will be beneficial in the long run. Your expenses will only include maintenance, repairs, and energy. AI and data analytics have also helped reduce production costs by providing insights and information to make the right production decisions. Automation helps improve productivity, quality and system performance, which in turn reduces your operating expenses (OpEx). At the same time, automated preventive maintenance can improve the life and performance of the machines. It enhances the value of your assets and in turn decreases your capital expenses (CapEx).

  1. Increases Productivity

Automated productivity lines consist of workstations connected by transfer lines. Each work station takes care of one part of the product process. Robotic process automation can be used to mimic many human actions. The system can be configured to login to applications and take care of the administrative work related to the business process. Robots can also be used on the production floor to handle raw materials, clean equipment, operate high-pressure systems, and do lots more. For example, in an automobile manufacturing unit, auto components are cut and shaped in different press working stations. All the parts are then brought together to one place where a robot puts them together to build the vehicle. Process automation greatly speeds up the production process.

  1. Enhances Quality

Industrial automation also helps increase and maintain consistent quality of the output. In manual processes, the error rate can vary considerably. On the other hand, automated machines in the manufacturing industry have an error rate that is as low as 0.00001%. Adaptive control and monitoring help check every level of the manufacturing process to reduce the margin of errors.

  1. Industrial Safety

A huge benefit of automation is improved safety at the workplace. Using robots for loading and unloading materials or transferring huge machine parts reduce risks of accidents. Safety curtains keep workers from going too close to the assembly lines or fast moving components, thereby improving safety. Thermal sensors continually check the temperatures in the production area. In case, they identify any spike in temperature, the sensors will send an alert. Immediately, precautions can be taken to ensure the safety of everyone on the production floor.

  1. Accurate Results

Data automation is based on accurate data integration and connectivity. When accurate information is used in the production process, you can be assured of precise results. AI and ML solutions help you get detailed data that can be analyzed using data analytics tools to get accurate information.

Deep learning algorithms are used to build self-healing digital grids that use data analytics and intelligent energy forecasts to manage energy generation. Machine Learning apps have been used to build a self-learning quality control system for assembly line. ML and AI solutions are scalable and self-learning. Both these features ensure that the automated systems deliver accurate results every time, without fail.

  1. Better Working Conditions and Value-Addition

One of the prime benefits of industrial automation is that it ensures consistent production and results. Computers, robots, and automated machines work at a steady pace. It allows you to have a better grip on the production rate. Automation not only delivers consistent production, but also consistent quality. In a flexible manufacturing system, the tools, processing machines, and material-handling robots are connected and controlled by a central computer system. Once the entire process is computed, the production goes on continuously without any drop in the pace or the results.

Flexible automation process lets you design or reconfigure a machine to suit a different product measurement or new product. In traditional production processes, it may take days or weeks to train employees. Another problem is that it can be difficult for workers to get used to the new process, which could cause production delays or quality issues. On the other hand, reprogramming a machine or a robot is easier and takes up less time. Plus, after a few trials, you will be ready to go into full production.

Automation frees up employees from working on tedious and repetitive tasks. This means that they can focus on other areas where they can do a value add. They can help with research and process development. Also, workers can effectively use robotic tools and machines to deliver faster and quicker results. Employees also experience the feel-good factor of doing positive and progressive work.

Industrial automation also helps improve working conditions. As the automated machines are able to step up the production, workers need not work long shifts or overtimes. Work hours are reduced, leading to an improved quality of life. In the United States, industries that adopted automation solutions were able to set a standard work time of 40 hours per week.

  1. Industrial Communication

Without industrial communication, industrial automation can be near impossible. The communication system helps monitor and operate entire production lines, manage power distribution, and control machines. Some of the popular protocols for industrial communication are Foundation Fieldbus, PROFIBUS, EtherCAT, EtherNET/IP, and CANopen. Industrial communication allows for faster data analysis and real-time decision making.

  1. Monitoring & Predictive Maintenance

A huge benefit of industrial automation is that it helps in monitoring and predictive maintenance. Production lines and production floor can be continuously monitored using sensors. These sensors track temperature, acoustics, time, frequency, oil pressure and other parameters related to the production process. If the sensors detect any change in these parameters, they will immediately send an alert. When the alert is received, the technicians can immediately identify the cause for the change. If it is noted that the changes in parameters may cause equipment problems or issues in the production process, then immediate service or repairs can be done. Automation can therefore help identify possible issues before they blow up into huge problems that can result in production downtime.

  1. Equipment Monitoring

An automated equipment monitoring system helps observe the working condition of all the equipment in the manufacturing unit. Sensors, cameras, and network can be used to observe the equipment from afar. The monitoring system also helps diagnose any issues in the equipment and do the necessary repairs and services. This automated solution can be effectively used in petrochemical plants, manufacturing units, and other industries where large and complex machines are used. The automated system enhances safety, reduces the number of operators on the floor, and improves machine performance and lifespan.

  1. Production Traceability

Automating the entire production process can greatly help in production tracing. Traceability is not only important in the food and beverage industry, but also in other industries. Tracing helps increase the quality and value of your product and facilitates mapping. Also, traceability makes root cause analysis more effective and aids continuous development. Automation helps trace the entire lifecycle of a product from the raw material to the location where the final product is shipped. You will also have a clear record of when and who did the product inspections, the assembly status, the condition of the machine when the product was processed.

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How can Utthunga help in Industrial Automation?

With more than 13 years of industry experience, Utthunga is a leader in industrial automation. Our team includes experts in the latest digital technologies and industry professionals to provide digital transformation solutions customized to your business process and business goals. A few of our industrial automation solutions include:

  • Deploying sensors, PLCs, controllers, etc. for automation
  • Using embedded engineering to create smart devices (non-digital to digital)
  • Offering data integration solutions that integrate plant floor assets to each other and other systems
  • Building applications (desktop, mobile) for commissioning, calibration, local diagnostics and configuration
  • Building bespoke application to enable integration of OT data to SCADA, MES, Enterprise systems
  • Creating analytics solutions

Contact us to know more about our industrial automation solutions.

Application of Embedded Systems in Industrial Automation

Application of Embedded Systems in Industrial Automation

by | Sep 8, 2021 | Blogs, Uncategorized

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.

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Machine monitoring:

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.

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Machine control:

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.

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Leverage Utthunga’s embedded systems capabilities

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:

  1. Localization and networking of all systems using energy-efficient systems that transfer only the required information
  2.  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:

  1. Product Design and Development:
    • End to end product development
    • Firmware, hardware application development
    • Electro mechanical product development
  2. Process Automation:
    • Metering application
    • Loop powered design and development
    • IS certification engineering service
    • Sensor integration and sensor application development
  3. Factory Automation:
    • Condition monitoring
    • IoT gateway
    • Edge computing
    • Enable legacy machines for IoT
    • Industrial protocol simulator
    • Wireless application development
  4. 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.

Artificial Intelligence in Industrial Automation: A Primer

Artificial Intelligence in Industrial Automation: A Primer

by | Jun 10, 2021 | Blogs

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.

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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.

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How AI impacts the Industrial Automation?

  • Get Valuable Insights from 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.

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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.

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When AI Goes Wrong?

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.