Lilach Goren Huber

Dr. Lilach Goren Huber is a Senior Lecturer and R&D Projects Leader at the ZHAW School of Engineering, specializing in predictive maintenance and data-driven solutions for industrial systems. She leads multiple projects focusing on AI applications in anomaly detection (e.g., wind turbines, solar power plants), physics-informed machine learning for sensor error correction, and fault prognostics under data scarcity. Her work bridges academic research with industrial implementation, emphasizing scalable deep learning frameworks for commercial fleets and energy infrastructure.

Research interests include predictive maintenance strategies, machine learning for industrial IoT, and hybrid prognostics combining domain knowledge with AI. She has published extensively in journals like International Journal of Prognostics and Health Management and Journal of Big Data, with a focus on renewable energy systems, SCADA data analysis, and cross-domain transfer learning.

• Current Projects: Physics-informed ML for elastomer sensors, end-to-end fault prognostics for power grids, and hybrid prognostics research.
• Past Contributions: Developed decision support systems for laser cutting machines, optimized hydroelectric maintenance schedules, and created risk-based frameworks for Swiss national road safety equipment.

She actively contributes to the Expert Group Smart Maintenance and serves as a project leader in the ZHAW-PARC initiative. Her work addresses technical challenges like data contamination, sensor calibration errors, and real-world implementation barriers in industrial AI adoption.