Xinze Lian

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About

Xinze Lian’s research focuses on the intersection of reliability engineering and robotics, with a particular emphasis on small-sample statistical modeling and parallel mechanism control. His most impactful contribution is a Bayesian reliability assessment framework for permanent magnet brakes (PMBs), which addresses the critical challenge of evaluating complex electromechanical systems under limited data. The proposed bivariate Wiener model, published in 2024, has already garnered 38 citations, reflecting its significance for industries ranging from automotive and aerospace to medical devices. This work provides a rigorous statistical foundation for predicting PMB failure times, enabling safer and more durable designs. In parallel, Lian has advanced experimental robotics through the development of an open-architecture control platform for 3-DOF planar parallel robots. By integrating a PC-based motion control card with a WinForm interface, his 2022 study offers a flexible, extensible solution for real-time robot control—a valuable resource for researchers and engineers seeking to prototype and test novel algorithms. Together, these contributions demonstrate Lian’s ability to bridge theoretical statistics with practical mechatronic systems, establishing him as an emerging authority in reliability assessment and robotic control.