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How can AI capabilities improve machinery performance challenges?

Jul-2024

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Sanjana Nair, Canvass AI, sanjana.nair@canvass.io

Combining the capabilities of MONET and Canvass AI creates a powerful solution for tackling machinery performance challenges. MONET allows subject matter experts to effortlessly analyze and interpret vast amounts of operational data, identifying patterns and trends that signal performance issues. This data-driven approach enables quick and accurate diagnosis of potential problems without the need for deep data expertise.

Canvass AI complements MONET by applying advanced machine learning algorithms to predict machinery failures and optimize performance. By leveraging predictive analytics, Canvass AI can forecast maintenance needs, reducing unplanned downtime and extending the lifespan of equipment.

Together, MONET and Canvass AI offer a comprehensive approach to machinery performance management. MONET's intuitive data analysis combined with Canvass AI's predictive capabilities ensure that machinery operates at peak efficiency, minimizing disruptions and maximizing productivity. This synergy not only addresses current performance issues but also proactively prevents future challenges, leading to significant cost savings and operational improvements.

Jul-2024
Lisa Krumpholz, Navigance GmbH (a subsidiary of Clariant Catalysts),

Machine learning algorithms in AI can significantly improve performance challenges in three key areas: continuous data stream analysis, real-time anomaly detection, and failure root cause analysis. Navigance harnesses the power of AI algorithms to automate data preprocessing, enrich data, and continuously analyse data streams, ensuring ongoing monitoring of machinery operations. By screening and interpreting vast amounts of plant data in real-time, along with dedicated algorithms, anomalies and suspicious machinery conditions can be precisely pinpointed, enabling plant teams to detect early signs of failures and deviations, facilitating proactive maintenance, and minimising downtime. Moreover, it enables a transition from preventive to condition-based maintenance, where maintenance decisions are based on the actual condition of assets. Additionally, AI-driven data analysis provides invaluable support in root cause analysis by analysing historical data and identifying patterns or correlations leading to failures.

Jul-2024
Philippe Mege, Axens, Philippe.MEGE@axens.net

There are several ways for AI to significantly enhance machinery performance, such as real-time monitoring by providing online recommendations to operators or control systems. Axens, through its proprietary Connect’In monitoring solution, has developed dedicated recommendation dashboards for asset optimisation based on client constraints and targets. Machine learning algorithms have been built and trained on historical data combined with first principles laws. These recommendations can also be managed in a closed loop through advanced process control, optimising performance and preventing issues from escalating. Targets for machinery can be energy efficient by adjusting power consumption based on demand and operating conditions and not at fixed parameters anymore, as could often be the case.

Another application developed by Alfa Laval and integrated into the Connect’In solution is the Performa module dedicated to adjusting feed-effluent heat exchanger parameters to predict when operating conditions are likely to lead to damage or failure. By identifying potential issues before they occur, issues can be prevented and/or maintenance can be scheduled proactively, minimising downtime and reducing the risk of unexpected breakdowns.

Connect’In is a mark of Axens.

Jul-2024