刮板输送机可靠性研究进展与未来发展趋势

Research progress and future development trends in scraper conveyor reliability

  • 摘要: 刮板输送机作为综采工作面的核心输送装备,重载输送、强落煤冲击等复合工况导致刮板输送机出现链条断裂、中部槽磨损及链轮啮合异常等问题。为明确刮板输送机可靠性研究方法及未来发展趋势,以关键结构性能退化与整机可靠性提升为主线,分析了链条、中部槽和链轮等关键部件的典型失效形式及主要诱因,从可靠性设计、可靠性制造、可靠性控制、运行监测与维护4个方面总结相关技术研究进展,包括:概率分析、故障驱动、物理仿真与优化等可靠性分析方法,以及面向关键部件的结构优化设计;重载输送工况下耐磨抗冲新材料研发、精密成形与焊接、热处理及表面强化技术;多驱平衡协调、链张力与动态载荷调控、煤流感知与自适应调速等刮板输送机控制技术;刮板输送机状态监测与故障诊断、可靠性评估、剩余寿命预测等运维管控技术。分析指出,现有研究存在复杂工况载荷表征不足、关键部件耦合作用机理不明、多源数据积累缺失及现场验证局限等问题。从数据驱动长寿命设计、智能多驱协同调控和数字孪生全生命周期管理3个方面展望了刮板输送机可靠性研究的未来发展方向,提出应依托智能矿山统一的感知和管控体系,将三者有机结合,形成设计、制造、控制、运行维护与信息反馈相互衔接的可靠性技术体系。

     

    Abstract: Scraper conveyors are core conveying equipment in fully mechanized mining faces. Complex operating conditions such as heavy-load conveying and intense impacts from falling coal can lead to chain breakage, middle trough wear, and abnormal sprocket meshing in scraper conveyors. To clarify research methods and future development trends in scraper conveyor reliability, this paper analyzes the typical failure modes and main causes of key components, including chains, middle troughs, and sprockets, with the performance degradation of key structures and the improvement of overall-machine reliability as the main thread. It also summarizes relevant research progress from four aspects: reliability design, reliability manufacturing, reliability analysis methods based on probabilistic analysis, failure-driven approaches, physics-based simulation, and optimization, together with structural optimization of key components; development of wear- and impact-resistant materials for heavy-duty conveying conditions, precision forming and welding, heat treatment, and surface strengthening technologies. Scraper conveyor control technologies include coordinated balancing of multiple drives, regulation of chain tension and dynamic loads, coal flow sensing, and adaptive speed control. Operation and maintenance management technologies include condition monitoring and fault diagnosis, reliability assessment, and remaining useful life prediction. The analysis indicates that existing studies are limited by inadequate characterization of loads under complex operating conditions, unclear coupling mechanisms among key components, insufficient accumulation of multisource data, and limited field validation. Future research directions are proposed in three areas: data-driven long-life design, intelligent coordinated control of multiple drives, and digital-twin-based life-cycle management. These directions should be integrated within the unified sensing and control system of intelligent mines to form a reliability technology framework that connects design, manufacturing, control, operation and maintenance, and information feedback.

     

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