Industrial plants have a wealth of data to draw on, coming from numerous sources, says Vikas Maurya.

Developments and advancements in Industry 4.0 and the Industrial Internet of Things (IIoT) have been in the making for over a decade. These developments are not only boosting the potential of industrial automation – but also changing how production plant operations and data management work, as ABB’s Global Product Line Manager for Edge, Cloud & IoT, Vikas Maurya, explains.

Industrial plants have a wealth of data to draw on, coming from numerous sources sometimes including thousands of sensors and control systems. Although these large volumes of data can serve as a goldmine that can be plumbed for information to increase the effectiveness of operations, safety and energy efficiency, they bring several challenges regarding aggregation, analytics, and practical application. Some examples of these challenges include scalability and difficulties with real-time analysis.

Increasing volumes of data mean new management solutions are required for industrial plants to use this information effectively. Although there is massive potential for optimising operations through data, many industrial plants struggle to use this data productively because of its complexity. Indeed, it is estimated that the average industrial plant uses less than 20% of its available data. It is crucial to find solutions that can seamlessly integrate with existing systems, improve real-time data processing, and help extract practical insights from large volumes of data.

Industrial plants need to find flexible and efficient solutions that can meet the potential of real-time data analysis. These solutions also need to be easy to integrate into existing systems, meeting the challenges of data diversity and operational scale.

Big data challenges

The complexity of data management lies in the amount, volume, and variety of information that it contains. Traditional methods of leveraging this data are becoming increasingly ineffective due to the quantity, volume, and variety of data that industrial plants deal with. Furthermore, the industrial sector faces other challenges like increased competition and costs. It means industrial plants need to optimise their operational efficiency to remain profitable. Some industries operate on thin margins where effective use of data will impact profitability, which leads to increased pressure in that regard.

There are also challenges with demographic shifts and technological adaptation. Workforces in industrial sectors are going through drastic changes, leading to issues with knowledge transfer and the adaptation of technologies to meet the skills of the newer generation.

As industrial operations become more complex, so does the data they generate. Industrial plants deal with information from various sources like sensors, pumps, actuators, and others. Industries are often already equipped with the right tools to collect massive amounts of data from these sources, but have issues analysing or leveraging this data effectively. Sources of data are often incompatible with each other, meaning the complexity of different sources of information prevents industrial plants from quickly analysing and applying their data to operational processes. To put this into perspective, if you were to have 2,000 devices with 100-200 sensors, with each collecting data every second, this would generate more than 2,000 terabytes of data per month.

The sheer amount of data being generated prevents industrial plants from quickly delivering processed insights to the appropriate stakeholders, meaning that they may miss out on opportunities to optimise industrial operations or prevent downtime. It is important that industrial plants analyse data quickly while ensuring the insights are actionable and effectively communicated across the organisation.

The role of edge computing

The increase in volume and complexity of information indicates that processing data requires an adaptive approach that focuses on localised processing. One solution is edge computing, which brings the computing power as close as possible to where the data is being generated—at the edge of the network.  The edge computing market is expected to be worth $45.4 billion by 2028, emphasizing its importance and utility in various industries. In practice, it changes how industrial plants approach data handling and processing processes by improving key aspects of data management and usage.

One advantage of edge computing is fast processing and response times. It provides ultra-low latency data processing since it is processed at the edge location. As such, it is ideal for fast-moving industries, where delays could bring on large-scale operational disruption or safety risks. Another benefit of deploying edge computing in industrial settings is the ability to process data where it is generated. This decentralisation reduces latency since data does not have to travel back to a server, making it useful for applications that require an immediate response like emergency shutdown systems.

Local processing increases security by limiting the distance that data travels, lowering the risk of interception and compromise. Sensitive information should be retained on-site or in an industrial facility, rather than being exposed to the internet or other networks.  It adds an extra layer of protection in industries that handle sensitive data and helps assure information security while complying with strict data privacy regulations.

Even though edge computing requires an initial investment, it minimises the amount of bandwidth required to send data to the cloud and reduces network expenses.  It also lowers subscription costs by reducing the need for more extensive cloud computing resources by processing data locally, making it a long-term cost-effective solution for industrial plants. In particular, renewable energy providers can save on time and travel costs by employing edge computing, as they often operate remote sites that are difficult to access.

Edge computing lets industrial plants quickly access and analyse data directly where it is generated. This helps industries where operational efficiency relies on real-time decision-making. As a result, edge computing derives actionable insights from IIoT devices. It enables continuous monitoring and adjustment of operations, improving the responsiveness and flexibility of dynamic industrial environments. With the Asia-Pacific region expected to account for the largest share of the edge computing market by 2027, we might see several industrial plants in India adopt edge computing due to the plethora of benefits it provides in terms of data aggregation and analysis.

Integration and system capabilities

Edge computing platforms effortlessly link with the existing control systems and IIoT infrastructure, thus affording smoother data exchange through the different

technology layers. This interoperability is vital for industries where new technologies must integrate with older ones so that industrial processes can function optimally without re-engineering.

Moreover, edge computing further underpins the flexibility of industrial automation solutions with different hardware and software from different vendors. Multi-vendor support means industrial plants are at liberty to choose the best technology available in the market and do not have to be bound to only one. In turn, the deployment of edge computing is based on operational needs, geographical constraints, and security requirements.

ABB Ability™ Edgenius, for example, offers a tailored solution to the challenges posed by the expansion of Industry 4.0 and IIoT within process industries. Edgenius can seamlessly integrate with existing IIoT infrastructure and control systems while bridging the gap between data generation and usage.

Edge computing can also take more than one approach; either deployed directly on-premises near the data sources or with a hybrid model having local processing and cloud-based analytics. It allows improved data flow within industrial automation systems, considering that it enables processed data at the edge of the network. Ultimately, the idea is that important data, which is easily available for analysis without latency, helps industrial plants make speedy and informed decisions.

For more information, visit: https://new.abb.com/process-automation/edgenius

Vikas Maurya is Global Product Line Manager at ABB, the leading provider of industrial automation and electrification solutions. With over 18 years of professional experience in product management and software development, Vikas has a strong track record of delivering value-added products and services that meet the needs of customers and stakeholders in various domains, such as Edge Computing, Cloud Computing, SaaS and PaaS Business models, Virtualisation, and Kubernetes deployments within production-class distributed systems. 

______________________________________________________________________________________________

For a deeper dive into the dynamic world of Industrial Automation and Robotic Process Automation (RPA), explore our comprehensive collection of articles and news covering cutting-edge technologies, robotics, PLC programming, SCADA systems, and the latest advancements in the Industrial Automation realm. Uncover valuable insights and stay abreast of industry trends by delving into the rest of our articles on Industrial Automation and RPA at www.industrialautomationindia.in