Owners and operators of critical infrastructure such as bridges, data centers and renewable energy assets are facing growing pressure to improve reliability and maximize uptime, while also managing ageing assets and meeting higher expectations for safety, compliance, and operational efficiency. Join this webinar to discover how physics-informed software and AI-enabled analytics are helping teams meet these objectives and make more confident predictive maintenance decisions.
Overview
Owners and operators of critical infrastructure such as bridges, data centers and renewable energy assets are facing growing pressure to improve reliability and maximize uptime, while also managing ageing assets and meeting higher expectations for safety, compliance, and operational efficiency.
Join this webinar to discover how physics-informed software and AI-enabled analytics are helping teams meet these objectives and make more confident predictive maintenance decisions.
You’ll see how engineering-grade sensor data, durability analysis, reliability modelling, and intelligent monitoring platforms can be combined to detect anomalies earlier, understand the physical meaning behind asset behavior, estimate remaining useful life, and prioritize maintenance before small changes become costly failures.
Drawing on examples from bridges, data centers, railways, and offshore wind, our engineering experts will demonstrate how the latest simulation and monitoring technologies provide timely alerts and contextualized insights to support confident decision-making in environments where reliability and uptime are critical.
Discover how to use physics-informed, AI-powered software to build Reliability, Availability, and Maintainability (RAM) digital twins and implement near real-time monitoring for predictive maintenance. We will also explore how teams can turn complex multi-sensor data into clear, reliable indicators of early structural degradation—helping them act before issues escalate into unplanned downtime or costly repairs.
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Key Takeaways
- Move from reactive to predictive maintenance by detecting early signs of degradation before they cause downtime.
- Learn how to use physics-informed AI to improve confidence by combining engineering models with real-world sensor data.
- See how to build RAM digital twins that support reliability, availability, maintainability, and lifecycle cost decisions.
Speakers
Dhora helps business leaders prevent costly operational disruptions by delivering advisory services and software solutions that enable continuous improvement in asset reliability, uptime, and lifecycle cost. Over his 13-year career, he has partnered with more than 60 asset-intensive organizations, including Fortune 100s, to transform how asset performance is managed. Dhora specializes in advanced analytics, operationalizing digital twins, and large-scale asset data management to deliver the impact required for Industry 4.0. His recent focus includes AI infrastructure (data centers and semiconductor manufacturing), autonomous transport reliability, and improving the profitability of aftermarket services for equipment manufacturers. He holds an engineering degree from the University of Waterloo. Outside of work, Dhora enjoys rolling up his sleeves renovating his century-old (1904) home in Toronto.
Gugaratshan leads a global team in developing advanced solutions for asset intelligence, monitoring, and reliability. He brings over 25 years of experience spanning engineering, product development, and industrial systems, combining deep technical expertise with proven leadership in building and scaling high-impact solutions. Gugaratshan began his career at Dana Corporation and later supported the development of gasoline and diesel engines as a test and research engineer. He went on to hold multiple technical and leadership roles at Caterpillar Inc., including managing manufacturing operations and leading product development teams. His expertise includes reliability and durability prediction, statistical methods, test instrumentation, uncertainty quantification, data acquisition systems, and structures and dynamics. He holds a BS in Mechanical Engineering from Trine University, an MS in Mechanical Engineering from Western Michigan University, and an MS in Applied Data Science from the University of Michigan, Ann Arbor.