Getting smart on industrial asset reliability: Future-proofing with artificial intelligence
Posted: September 26, 2024
AI is having a moment. While a vocal minority dismisses the technology as overhyped, the applications for AI-powered industrial solutions are nearly limitless: Whether organizations are looking to optimize asset reliability and performance, drive operational improvements, or accelerate R&D (not to mention the many applications that haven’t yet been undiscovered), there’s no shortage of ways you can put this innovative technology to work.
That said, despite the deluge of new think-pieces, articles, and explainers, for many, questions about industrial AI are still swirling: What is industrial AI? What industrial data architecture do I need to get the most out of my AI solutions? Which types of AI are best suited for industrial asset reliability? What do I need to get started?
As AVEVA’s Global Head of AI & Advanced Analytics, the responsibility of answering these questions often falls to me and my colleagues—which is precisely where the idea for this round-table discussion originated. Over the course of this five-article series, you’ll hear from four experts who will delve into crucial requirements, purpose-built types, and exciting new innovations encompassing the evolving world of industrial AI.
What are knowledge graphs?
The type of AI you’re likely familiar with (OpenAI’s ChatGPT, Google’s Gemini, and Microsoft’s Copilot, to name a few) is built around large-language models (LLMs). By way of a most basic definition, these generative AIs take in large datasets (language, in this case), and then organize and structure complex connections between those data points.
AI-powered analytics built on big data and knowledge graphing principles are hardly new. Here at AVEVA, for example, we’ve been perfecting models that power our predictive analytics and other AI- and ML-driven solutions for over two decades, and, in that time, we’ve been innovating a holistic portfolio of AI-driven solutions that work together.
In the wake of ChatGPT’s explosive unveiling, however, many startups and newcomers to industrial software have tried to replicate the AI chatbot’s disruptive success. They created generative AIs for industrial asset reliability, claimed to have pioneered groundbreaking new technologies, and marketed their solutions heavily. Behind the glossy advertising, though, they had only reinvented the wheel, albeit with an industry focus.
As industrial leaders look to build secure, scalable, and future-proof tech stacks, they should be wary of adopting untested solutions. Simply put, no matter how effective a knowledge-graphing AI is at a particular task, as organizations look to scale, they quickly become point solutions—which necessitate hours of coding to integrate with other systems, information sources, and parts of your business as you grow.
Knowledge linking and Industry 5.0: Taking knowledge graphs to the next dimension
Knowledge linking is the pinnacle of integrated AI-driven industrial technologies. Simply put, knowledge linking connects disparate data sources—some of which are individually AI-enabled—to provide comprehensive, context-aware recommendations.
For example, a generative AI point solution built on a knowledge graph might satisfactorily answer a question like “What was Unit 1’s maximum output last month?” A knowledge-linked suite of solutions can answer “What are my current plant issues?” followed by “What steps should I take to resolve them?” And, in the not-so-distant future, knowledge-linked solutions will be able to answer “How can I optimize my plant?”
It’s difficult to overstate how revolutionary this technology will be. It’s not just about using predictive analytics to optimize asset reliability, gaining real-time visibility into operational, enterprise, and sustainability data, or empowering your workforce. It’s about creating a comprehensive, dynamic map that links all these components together.
How do you progress to AI knowledge-linking?
Companies need to start with a bird’s-eye view of their needs. Organizations should work to identify how reliable data, predictive analytics, and other AI features can enhance their operations. From there, they can drill down into specific areas that will benefit from AI intervention.
It’s not just about implementing technology for technology's sake; it’s about ensuring that you can integrate AI in a way that supports your broader business goals. In this context, knowledge linking is the bridge that connects various data streams, making AI and other analytic systems smarter and more intuitive.
To progress to knowledge linking, you need to begin with a reliable data foundation. Stay tuned for the next blog in this series, in which my colleague Lora O’Haver will walk you through what to look for in a hybrid data infrastructure.
Key takeaways:
- AI in industry—AI is transforming industrial asset reliability, performance, and operational improvements, with vast applications ranging from predictive maintenance to operational efficiency to sustainability improvements to R&D.
- Beware untested solutions—Many new AI-driven industrial solutions on the market may be overhyped and are often just repackaged existing technologies. Companies should be cautious when adopting unproven solutions and should have well-defined goals for success.
- Scaling AI requires integration—While AI knowledge graphs are effective for specific tasks, as organizations scale, these solutions can become isolated. Successful scaling requires integrating AI across systems for comprehensive functionality and building it on a robust, scalable data platform.
