Self‐Lubricating and Surface‐State‐Modulated p‐p Heterojunction for Robust In Situ Sensing and Deep Learning‐Enabled Condition Identification

ABSTRACT The rapid expansion of Internet of things (IoT) and cyber‐physical systems presents a formidable challenge for sustainably powering massive numbers of distributed sensors. Friction, a ubiquitous phenomenon typically viewed as a source of energy dissipation, offers a novel avenue for energy harvesting and in situ sensing. In this study, a self‐powered sensing system integrating a self‐lubricating p‐p heterojunction DC generator (SHDG) is constructed from the friction interface between a hydrogenated diamond‐like carbon coating (HDLC) and a p‐type gallium nitride (pGaN) wafer. The SHDG exhibits a peak power density of 2.1 kW m −2 and an 85% reduction in the wear rate compared to metal‐pGaN counterparts. Theoretical analysis revealed that material transfer can modulate the pGaN surface states attenuating the built‐in electric field and thus augmenting the tribo‐induced electric‐field‐dominated DC output. Furthermore, the SHDG is integrated into a bearing for the high‐precision monitoring of dynamic parameters, such as cage slip, exhibiting an average deviation of 0.0014 Hz from commercial sensors. Coupled with deep learning, self‐sensing signals were utilized for fault diagnosis achieving an average accuracy of 96.81% across various conditions. The successful deployment of a smart bearing in a transmission system featuring wireless monitoring and stable operation exceeding 12 h corroborated its feasibility and durability. This study establishes a new paradigm for developing high‐performance, long‐lifespan, and self‐powered sensing systems for next‐generation intelligent equipment and IoT terminals.