High-frequency algorithms harness the power of digital signal processing for Condition Based Monitoring

Keith Holdaway, Advisory Industry Consultant at SAS, brings a unique blend of technical expertise and global perspective shaped by a passion for continuous learning across arts and sciences. With a deep belief in living life fully and embracing diverse experiences, Keith applies this mindset to the world of data and analytics. He shares his views on how high-frequency algorithms can unlock the full potential of digital signal processing for Condition Based Monitoring (CBM). He explores how advanced analytics and machine learning can detect subtle patterns in real-time equipment data, enabling predictive maintenance and reducing operational risk.

He will share his views on harnessing digitalization, predictive and explanatory AI to transform the way maintenance is planned and executed. In today’s fast-paced industrial landscape, efficiency and reliability are non-negotiable. By leveraging cutting-edge AI technologies, organizations can shift from reactive to proactive maintenance models. Predictive AI helps forecast equipment failures before they occur, while explanatory AI provides insights into why certain failures happen, enabling smarter and faster interventions. This synergy minimizes costly downtime, reduces manual errors, and optimizes the use of critical assets. The result is a more resilient, data-driven maintenance approach that not only cuts costs but also extends the lifecycle of systems and infrastructure.

1. Harnessing digitalization, predictive and explanatory AI, improve efficiency, accuracy, and cost-effectiveness of your maintenance processes, while reducing downtime and maximizing lifespan of both equipment and systems.