2024 Schedule

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Advancing Predictive Maintenance: AI-Based Solutions for Complex Machinery

Presenter: Yun Yao
President, Soralink
Predictive maintenance has posed significant challenges for the past three decades, particularly in handling machinery with variable speeds and diverse usage cycles. Traditional methods often fall short when adapting to the unique demands of such equipment, necessitating a more flexible and intelligent approach. Our solution leverages an AI-based analysis framework designed to address these complexities effectively, particularly in environments with stringent operational requirements. This presentation will explore four distinct use cases within the food processing and pharmaceutical industries, demonstrating how our adaptive, AI-driven methodology offers substantial improvements over conventional predictive maintenance techniques. Each case study highlights a scenario where our technology has successfully anticipated equipment failures, allowing for timely interventions that mitigate downtime and enhance operational efficiency. 1. Burnt Bearing Detection in Boiler Fans (Food Processing): We'll discuss how our system detects early signs of bearing failures in high-temperature environments, preventing catastrophic failures in critical boiler fan operations. 2. Belt Drive Malfunctions (Food Processing): Our AI framework identifies inefficiencies and emerging issues in belt-driven systems, enabling preemptive maintenance that ensures continuous production without unscheduled stops. 3. Integrating Weather Indicators for Temperature Tracking: This case examines the integration of external weather data with internal temperature readings to predict potential system overloads, enhancing the responsiveness of maintenance protocols. 4. Linear Motor Monitoring (Pharmaceuticals): Focused on the high-stakes pharmaceutical sector, we demonstrate rapid response capabilities in monitoring linear motors, crucial for maintaining the integrity of delicate production processes. Through these use cases, our presentation will illustrate the effectiveness of AI in transforming predictive maintenance from a reactive to a proactive strategy, emphasizing improved reliability, reduced costs, and enhanced safety across various industrial sectors.
About the Presenter:

Dr. Yun Yao, Ph.D. in Electrical Engineering from McGill University, is the president of Soralink, a dynamic and innovative company focused on addressing the challenges of predictive maintenance using AI and IoT. Utilizing the latest advancements in AI, wireless, and embedded computing, Soralink offers solutions that enhance predictive maintenance practices and increase operational efficiency.
Before founding Soralink, Yun spent over a decade at Clear-Com, where she led the development of next-generation wireless intercom systems and spearheaded numerous innovation initiatives. Her leadership and technical expertise significantly advanced the field of mission-critical intercom systems.
In addition to her industry experience, Yun co-founded Tactile Labs, a non-profit organization dedicated to advancing haptic research, an extension of her doctoral studies at McGill. Her work there cultivated a collaborative environment and drove innovation in haptics, demonstrating her dedication to pushing the boundaries of technology and enhancing practical applications.
Yun’s extensive background, blending industry and non-profit experience with entrepreneurial zest, propels Soralink’s core mission. Focused on the intricate challenges of predictive maintenance, she is committed to introducing cutting-edge technology into the manufacturing sector, with a vision to transform industrial operations by reducing waste, boosting efficiency, and enhancing worker safety.