IT in Manufacturing

The role of AI in industrial plants

June 2023 IT in Manufacturing

The average modern industrial plant uses less than 27% of the data it generates, according to industry experts at the ARC Advisory Group, Boston. Typically, the remaining 73% of data – much of it produced by plant process-control systems as high-frequency operational control (OT) data – is seldom used. Large volumes of other valuable functional data resides in a company’s general business or IT systems, and still more in the engineering systems (ET), covering specific design information for various assets. In addition to being rarely used, all this data is normally scattered about in separate silos and networks that support little or no cross-referencing.

“This is where the golden opportunity lies, which we can now unlock with new software platforms that simplify better convergence and analysis of OT/IT/ET data,” says Charles Blackbeard, business development manager of ABB Ability Digital. The benefits can be impressive, such as higher production rates from existing assets, less downtime because of predictive maintenance practices, safer operation, reduced energy and other raw material inputs, and lower environmental impact.

Improved convergence of OT/IT/ET data means bringing together previously separate elements, which have now been streamlined and integrated. To achieve this, all OT, IT, and ET data is accumulated in a data lake. Next, related data is contextualised and stored in an industry-specific data model, such as paper making or plastic extruding. Then advanced analytics and industrial AI algorithms are applied to identify correlations not previously visible.

“Industrial AI can play a major role in identifying these patterns and making process predictions,” says Blackbeard. The terms AI and ML are often used interchangeably, which can be confusing at times. AI is the overarching science of making machines and physical systems smarter by embedding artificial intelligence in them. ML is a subset of AI that involves systems gaining knowledge over time through self-learning to become smarter and more predictable, without human intervention.

“As an example, consider a motor, an essential and omnipresent asset in any plant. The motor generates a lot of operational data such as temperature, pressure and flow rate data from various stages of the production process. To acquire a holistic overview of the motor, we integrate information from all these systems and store the relevant pieces in a contextualised data model. This allows us to visualise and activate optimum equipment operation for the best overall process results,” explains Blackbeard.

In a large plant, there can be hundreds of such assets performing many functions and running under different operating conditions with varied design parameters, all with data stored in various systems. Widespread OT/IT/ET integration and contextualisation is therefore critical to obtain a complete view of the plant and carry out valuable analytical tasks that improve operations, asset integrity and performance management, safety, sustainability, and supply chain functions. What emerges are patterns that accurately predict future behaviour, allowing improved process performance.

“We have been using AI/ML to deliver a higher degree of prediction accuracy and optimisation to operations, processes and assets. Combining AI with deep industrial domain expertise empowers operators to run their industrial processes safely, more effectively and more sustainably,” notes Blackbeard. He adds that there are several barriers, perceived and otherwise, that hinder the implementation of advanced analytics. The most common reason for hesitation is the perceived complexity. People mistakenly think it is much more difficult to achieve than it is. Another explanation is the incorrect belief that, to use big data, you must make massive capital expenditures, because it is an ‘all or nothing’ undertaking.

“But it is not. You can start with small steps,” points out Blackbeard. Other reasons might be lack of cooperation between OT, IT and ET people, and just generally slow adoption of new digital tools in many industrial sectors. The fact is that it is easy to join this digital maturity journey, no matter where you are, using data and signals that are already available in your process control, business and engineering systems,” he concludes.


Share this article:
Share via emailShare via LinkedInPrint this page

Further reading:

Bringing brownfield plants back to life
Schneider Electric South Africa IT in Manufacturing
Today’s brownfield plants are typically characterised by outdated equipment and processes, and face challenges ranging from inefficient operations to safety hazards. However, all is not lost, as these plants stand to gain a lot from digitalisation and automation.

Risks facing the engineering sector
IT in Manufacturing
The engineering, construction, and real estate sector is facing significant challenges in the year ahead, with natural catastrophes, fire and explosion risks emerging as the primary concerns, according to the Allianz Risk Barometer.

First battery-electric trolley truck system for underground mining
ABB South Africa Motion Control & Drives
Boliden, Epiroc and ABB have passed a new technology milestone by successfully deploying the first fully battery-electric trolley truck system on an 800-metre underground mine test track in Sweden. This means the mining industry is a step closer to realising the all-electric mine of the future, with sustainable, productive operations, and improved working conditions.

African data centres: if you build it, they will come
Schneider Electric South Africa IT in Manufacturing
Africa’s data centre market is growing at an unprecedented rate, driven by a soaring demand for digital services, artificial intelligence, crypto currencies and cloud computing. This is good news indeed, as Africa’s burgeoning digital landscape also presents significant opportunities for investors, technology companies and local businesses.

When cyber attackers are using AI, your defence needs to do the same
IT in Manufacturing
Cyberthreats have become increasingly sophisticated, thanks to the use of artificial intelligence (AI), and attacks can now be executed rapidly and scaled beyond anything a human is capable of. Add in machine learning (ML), and attacks can now adapt and evolve in real time, becoming more sophisticated and stealthier. Traditional security measures are simply no longer effective; we need to counter the offensive AI with the use of defensive AI.

Closed-loop production chain for metal additive manufacturing
Siemens South Africa IT in Manufacturing
AMAZEMET has adopted solutions from the Siemens Xcelerator portfolio of industry software to help build its etal additive manufacturing materials and supporting post-processing equipment.

Edge computing: Introducing AI into the factory
Editor's Choice IT in Manufacturing
As AI evolves, it is evident that the most powerful models will be cloud-based, and hosted in data centres that are beyond the control of the average business. The practical application of AI in manufacturing control and automation will only be possible if some of the computing workloads can be brought onto the plant, inside the firewall and inside the plant network.

The magnificent seven of industrial software development
Schneider Electric South Africa IT in Manufacturing
There’s fast paced, and there’s supersonic, and the latter certainly applies to the evolution of software or, more specifically, industrial software. The last year has seen the industrial software step to the fore to take over the mundane, repetitive and sometime dangerous, allowing us to focus once again on what makes us uniquely human.

Transforming the electromechanical landscape
ACTOM Electrical Machines IT in Manufacturing
The electromechanical industry is fundamentally being transformed by Industry 4.0, which is ushering in an era of more efficient and innovative practices. Increasingly, companies are integrating automation and AI to optimise manufacturing processes, enhance productivity, and deliver better solutions to clients.

Automated machine health monitoring
SKF South Africa IT in Manufacturing
Coupled with rapidly advancing technologies, the growing global population is propelling an ever-increasing demand for essentially anything that consumers require, from infrastructure to food. By switching from a manual to an automated machine monitoring system and data collection process, operators will increase the availability of their rotating equipment, and subsequently optimise their operations.