Abstract: Heavy-duty gas turbine was the significant equipment in clear energy, and the vibration level of the shafting system is a visual representation of the operating states. Tripping faults were as a kind of unplanned sudden shutdown triggered by increasing vibrations, which would cause a large impact on the core components of the gas turbine, such as blades and tie rods, resulting in equipment damages. A method for predicting the vibration trend of heavy-duty gas turbines was proposed based on improved particle filter. By analyzing the particle filter, a secondary resampling strategy was proposed to make the improved particle filter more resistant to particle degeneracy and improve the adaptability of particle filter. The improved method was verified in a tripping fault of a 300 MW heavy-duty gas turbine, which shows a superior prediction accuracy of tripping fault time. The proposed approach may guide the control strategy of gas turbines.

Key words: heavy-duty gas turbine, tripping fault prediction, improved particle filter, secondary resampling

摘要： 重型燃气轮机是清洁发电的重要装备，其轴系的振动水平是机组运行状态的直观表征。跳机故障是由于振动加大而触发的非计划突然停机，会对燃气轮机的核心部件（如叶片、拉杆等）产生较大冲击，造成设备损伤。提出基于改进粒子滤波的重型燃气轮机振动趋势预测方法，通过对粒子滤波方法的分析，提出一种二次重采样策略，使得改进粒子滤波对粒子匮乏现象更具抵抗力，具有更好的适应性。所提方法在某300 MW重型燃气轮机的跳机故障中得到验证，能够准确预测跳机的故障时刻，为燃气轮机的控制策略调整提供指导。

关键词: 重型燃气轮机, 跳机故障预测, 改进粒子滤波, 二次重采样