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AI in Integrated Control Systems: Challenges and Opportunities

As we step into the fourth industrial revolution, AI/ML has gained traction across the industrial landscape for enhancing visibility, managing risk, and optimizing operational processes by analyzing different data patterns. Modern methods and technologies have essentially interlinked physical assets through replicating them as a digital twin, thereby ensuring a seamless flow of operational data which in turn can be utilized by organizations to operate more efficiently, hit their production targets, ensure safety across the board, and scale their businesses.

Implementing AI/ML does come with certain caveats in terms of adaptability, infrastructure availability, and financial viability, depending on your unique context. However, in a landscape where everyone is leveraging innovative tools and technologies just to gain the slightest edge, can you truly expect to remain competitive in the long run?

AI/ML has been helping industries in accelerating their digital evolution. Leading OEMs have built a technological stack consisting of intelligence modules that can seamlessly be installed and configured in the control system, and a web-based interface to build learning models on existing data. Data from the module flows into the web-based interface, where models are rigorously trained, and ontologies are utilized to explain data relationships before predicting outcomes through testing hypotheses or running scenario analysis on control variables.

The ability to process your data in real-time not only allows effective decision-making but also has an impact on multiple areas. For instance, models can swiftly detect anomalies or variances in preset control variables that have led to declining production rates over time. They can then be optimized to increase production to the desired level. Similarly, these models are even flexible enough to come up with exact control variables to adjust for frequently changing market demand. On the control systems maintenance front, it can be used for proactive monitoring to prevent future asset failures, thereby significantly minimizing operational downtime and costs.

The horizon is wide open for artificial intelligence and deep learning. The next step in evolution of this technology is integrating deep reinforcement learning (DRL)-based controllers, having the ability to learn from hundreds of simulations from different plants. This could essentially lead to operating closely to a human brain, with controls extending beyond automated systems to evaluating sensory information and regulating key variables accordingly.

The picture we have painted above is undeniably captivating, yet it also brings forward its own set of challenges that may deter many. Implementation of AI/ML requires a set of pre-requisites which need prior deployment.

  1. Upgrades of legacy control systems:
    Legacy systems often work in silos and have either lower quality or lower accuracy of data, deeming them unfit to be utilized for any complex analysis. Moreover, they face compatibility issues with the latest modules and are vulnerable to security threats due to outdated features.
  2. Data Convergence:
    Data consolidation into a centralized library or database is crucial for establishing meaningful relationships before accurately predicting outcomes.
  3. Integration:
    The implementation of the whole technological stack with the right infrastructure can be tricky and can require a considerable financial investment to go along with it.

Steps to implement AI

Implementing AI/ML involves a systematic approach to ensure successful integration into existing systems. Here are key steps to consider:

  • Start off by clearly outlining goals and objectives you hope to achieve with AI/ML implementation. Your approach should involve conducting a comprehensive assessment about the existing system and identifying areas where you can attain maximum value or “bang for the buck”.
  • Build a robust infrastructure featuring a strong data management system capable of seamless integration with the web-based interface for analysis. Additionally, upgrade compatible sensory technology to complement the system.
  • Train existing workforce to maintain system integrity, ensuring accurate data collection and analysis.
  • Conduct tests to evaluate AI/ML compatibility and integration with existing systems to ensure seamless operation. Begin on a small scale to obtain accurate and reliable results.
  • Continuously monitor performance and define key performance indicators to measure AI/ML effectiveness.

Conclusion

In conclusion, AI/ML is a leap forward for control system technology that is yet to realize its true potential. If the infrastructure is in place, it can significantly accelerate your digital transformation journey, allowing you to take quick and informed business decisions, and continue to have a sustainable competitive advantage in the market.

If this is something that falls outside your area of expertise, you can always leverage our experience. Our multi-vendor and multidisciplinary capabilities, along with our partnerships with leading OEMs in this technology stack, can ensure seamless integration with your existing infrastructure within predetermined timelines.

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