基于优化极限学习机的涡轴发动机转子碰摩故障诊断

针对涡轴发动机容易在转子过渡态-稳态间瞬间失衡导致碰摩现象，提出改进遗传算法优化的极限学习机诊断模型。基于某涡轴发动涡轮机匣振动信号包络曲线，仿真涡轴发动机正常状态、燃气涡轮转子碰摩状态、动力涡轮转子碰摩转态、燃气与动力涡轮转子碰摩转态4种工况的振动信号；对振动信号进行频谱分析，提取振动信号特征参数构建故障样本数据集；使用改进遗传算法优化极限学习机，并将它用于碰摩故障诊断。结果表明：训练集平均诊断准确率为96.8%、波动幅值为2.82%；测试集平均诊断准确率高达95.43%、波动幅值为0.93%，收敛误差达到0.22，验证了所提出的方法诊断准确率高、波动幅值小、误差低，适用于碰摩故障诊断。

Fault Diagnosis of Turbo-Shaft Engine Rotor Rubbing Based on Optimized Extreme Learning Machine

Aiming at the rubbing phenomenon caused by the transient imbalance of the turbo-shaft engine between the rotor transition state and the steady state, an improved genetic algorithm (GA) optimized extreme learning machine (ELM) diagnosis model was proposed. Based on the vibration signal envelope curve of a turbo-shaft engine turbine casing, four working conditions, the normal state of the turbo-shaft engine, the rubbing state of the gas turbine rotor, the rubbing rotation state of the power turbine rotor and the rubbing rotation state of the gas and power turbine rotors were simulated; the frequency spectrum of these vibration signals were analyzed, and the characteristic parameters of the vibration signal were extracted to construct a fault sample data set; an improved genetic algorithm was used to optimize the extreme learning machine, and it was used in rubbing fault diagnosis.The results show that the average diagnosis rate of the training set is 96.8%, the fluctuation amplitude is 2.82%; the average diagnosis rate of the test set is 95.43%, the fluctuation amplitude is 0.93%, and the convergence error reaches 0.22.The method proposed has high diagnosis rate, small fluctuation amplitude and low error, which is suitable for rubbing fault diagnosis.