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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.

Tools and Technologies for Efficient Asset Management in Industries

Tools and Technologies for Efficient Asset Management in Industries

by | May 29, 2021 | Blogs

What is Industrial Asset Management?

An industrial asset management system helps manage the physical assets of your plant such as machines, devices, equipment, network and operations. An industrial asset management system is essential for all the industries, discrete or process, where any minor fault in the assets may lead to expensive delays, shutdowns, plant operation interruptions, quality issues, security threats, data loss, and a lot more.

By deploying a smart industrial asset management system, the plant operators can be assured of uninterrupted plant operations with greater asset throughput.

Industrial asset management consists of services, systems, and software programs that enable you to monitor and control the operational cycle. Artificial intelligence systems and IoT solutions can be efficiently used in setting up an enterprise asset management system. The various features of an asset management system include planning, scheduling, work management, asset maintenance, supply chain management, financial management, environment management, materials management, and information management.

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Types of Industrial Asset Management

The 3 major types of asset management are:

  1. Traditional Asset Management: The assets are monitored and managed by an expert. He/she identifies the deviations in the asset performance based on their experience and takes corrective measures to resolve the issues. This kind of asset management requires immediate corrective action.
  2. Periodic Inspection: This type of asset management is based on various predetermined parameters. Plant maintenance personnel conduct this assessment at frequent intervals based on the performance and maintenance requirements.
  3. Online Asset Management: The latest trend in the asset management process is Online Asset Management. Here, the sensors and other devices are used to capture real-time data from the assets deployed on the plant floor. All the data is sent to a central system, where it is processed to identify the deviations from the defined indicators and corrective actions are taken to resolve the issues using a blend of automation and manual intervention.

These three types of asset management processes look at the current condition of the asset or process. They don’t evaluate the future performance and lifespan of the asset. A systematic enterprise asset management system provides a comprehensive approach to evaluating the assets and enables the stakeholders with the ability to make informed decisions.

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Why Industries Need Asset Management?

There are various reasons why industries must deploy a smart asset management system in the plants:

  • To meet the stringent safety requirements. This is especially important for process industries as the plants need to meet intrinsic safety requirements without fail.
  • To setup up preventive and regular maintenance procedures to ensure optimum performance of the equipment
  • To track the lifecycle of the assets
  • To allocate a budget based on future predictions made from the available data
  • To enhance performance reporting and compliance
  • To keep pace with the dynamic customer requirements and demands

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Various Ways to Implement the Asset Management

You will need the following to set up an efficient industrial asset management system in your plant:

  • Data Collection: Reliable data procured from sensors, manual inspections, as well as IIoT devices.
  • Edge Devices: Edge devices are required to gather data from the sensors and process it partly on its own and pass it to the cloud when necessary.
  • Remote Asset Manager/Edge Manager: A remote asset management system with abilities to monitor and manage the health of the assets over the air.
  • HMI (Human-Machine Interface): An HMI (human-machine interface) can be used to gather data in one place, connect the system with the correct database, oversee KPIs, monitor machines, track production times, track tags, and visually display data.
  • Efficient Analytical System: An appropriate analytical system can be implemented to track and analyze the device performance at various times and define indicators to detect deviations in near real-time.

Challenges in Implementing an Asset Management System

Implementing any new process will definitely see many challenges with respect to people, technology, and process:

  • Acceptance by the Employees: The biggest challenge regarding people is the resistance to new implementations and changes. Employees need to be elaborated on the benefits of implementing the enterprise asset management system and how it will help make their work more efficient.
  • Not Considering Sub-processes in Addition to the Main Processes: Companies may face challenges in the process if the asset management policy was drafted without considering the entire process along with the sub-processes. If the policy was drafted only from the perspective of the asset manager, it may be challenging to integrate it into the process. Therefore, the processes and subprocesses need to be considered while building an asset management policy and plan.
  • Learning Curve: The use of an automated asset management system can be challenging to people who have been following manual methods for years. Employees need to be provided proper training and ample time to adapt to the new model to overcome this challenge.

Trending Tools and Technologies Used in Asset Management

Asset managers in the manufacturing and processing industries will be expected to be more tech-savvy in the future. Some of the technologies to be seen in upcoming years for managing assets are:

  • Data-driven Asset Management: Managing assets in modern plants is more data-driven. With smart sensors, edge computing, and tinyML in place, gathering plant floor data and processing it on the edge will yield better results. On the other hand, cloud computing will allow more dynamic implementations and allow processing even larger and distributed datasets providing a comprehensive view to the entire data.
  • Robots and Automated Bots: Automation of most processes of asset management can be enabled by the use of advanced robots and automated bots. These AI bots are used in places that are not easily accessible or are potentially harmful to humans.
  • High Definition Cameras: Image sensors are widely used in machine vision cameras. These image sensors are used for a wide range of applications in robotics, pattern recognition, information detection, etc. The need for high performance, low-footprint, and reliability has paved the way for integrated sensors that combine all the image sensing functions. For example, an Infrared Imaging sensor can operate irrespective of the light conditions. Due to this ability, IR imaging sensors are used in marine, military, air forces, etc., to capture thermal images of the objects.
  • Artificial Intelligence (AI): Modern manufacturers are paying attention to introducing the latest technologies to improve the production processes. Using AI techniques in process modeling, optimization, debottlenecking, troubleshooting, etc. has become quite common nowadays. For instance, plant owners can use AI models to learn the normal behavior of the plant using historical data and detect the anomalies during operation. A root-cause analysis can be carried-out to draw inferences that can be used to implement in actions.
  • Augmented Reality (AR): Augmented Reality is comparatively a newer technology and industries are not yet prepared to adopt this rising trend. However, AR has a vast range of applications that can prove to be beneficial for the process industries. Whether it is managing their warehouse for object recognition or to check the system failures. Imagine, diagnosing a boiler with the help of AR glasses. This can be really helpful in minimizing the time consumed in inspecting the machines and equipment.