The transition to carbon neutrality calls for reliable industrial equipment and more efficient use of resources throughout its service life. Intelligent predictive maintenance can support this transition by informing maintenance decisions and extending equipment lifetime. This talk presents recent advances in remaining useful life prediction and health management for complex industrial systems, covering model-based methods, data-driven learning, and approaches that integrate degradation models with operational data. Particular attention is given to limited degradation data, changing operating conditions, uncertainty quantification, and model interpretability. Case studies involving lithium-ion batteries, turbofan engines, and power electronic converters illustrate methods for degradation prediction and lifetime extension. It concludes by discussing how these methods can support longer asset life, reduce premature replacement, and contribute to more sustainable industrial and energy infrastructure in the pursuit of carbon neutrality.

Jiusi Zhang is currently an Associate Professor with the School of Automation Engineering, University of Electronic Science and Technology of China. He received the B.E. degree in automation from Harbin Engineering University, Harbin, China, in 2019, the M.Sc. and Ph.D. degree in control science and engineering from Harbin Institute of Technology, Harbin, China, in 2021 and 2024. His research interests include fault diagnosis and prognosis, predictive maintenance, reliability and safety, data-driven monitoring and optimization, prognostics and health management, and their intelligent applications on industrial systems.