Papers
1
Total Citations
482
H-Index
1
About
Dr. Qiang Miao is a leading figure in prognostics and health management (PHM), with a particular focus on the reliability and safety of complex engineering systems. His research centers on developing advanced algorithms for fault diagnosis, remaining useful life (RUL) prediction, and condition-based maintenance. Dr. Miao’s most impactful contribution is his pioneering work on battery health management, exemplified by his highly cited 2012 paper on using an unscented particle filter for lithium-ion battery RUL prediction, which has garnered over 480 citations. This work established a robust framework for accurately forecasting battery degradation, directly influencing the design of safer and more efficient energy storage systems for electric vehicles and portable electronics. Beyond this landmark study, his broader research integrates signal processing, machine learning, and stochastic modeling to enhance the predictive capabilities for rotating machinery and electronic systems. Dr. Miao’s work is widely recognized for bridging theoretical innovation with practical industrial applications, making him a key authority in the field of intelligent maintenance systems.
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Top Papers
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