[IMC 2025] Edge-Intelligent Sensing for Predictive Maintenance
Автор: IMOS
Загружено: 2025-09-16
Просмотров: 91
Описание:
Dr. Michele Magno, ETH Zurich
As predictive maintenance shifts from reactive strategies to proactive intelligence, the convergence of novel sensing technologies, including ultra-low-power, neuromorphic, and energy-harvesting sensors, with Edge AI is transforming the field. In this talk, I present an AI-based approach to predictive maintenance, driven by fast, local decision-making and energy-efficient sensing architectures tailored for battery-operated devices as well as robotic platforms. Drawing from our research at ETH Zurich, I will showcase embedded and efficient intelligence across multiple systems, from self-sustaining sensor nodes to autonomous robotic drones and dogs equipped with multimodal perception capabilities (radar, depth, and inertial sensing). These platforms enable robust condition monitoring, anomaly detection and predictive maintenance in dynamic environments, leveraging real-time edge processing to reduce latency, bandwidth, and energy consumption. Finally, I will explore how sensor fusion, neural model optimization, and custom hardware-software co-design unlock scalable, sustainable solutions for the next generation of maintenance in industrial, agricultural, and infrastructure monitoring domains.
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