基于循环平稳时间序列的齿轮裂纹故障早期检测
Early Detection of Gear Crack Based on Cyclic Time Series
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摘要: 在分析齿轮振动信号的基础上 ,结合其具有循环平稳性的特点 ,提出了基于线性几乎周期时变 AR模型的故障早期检测方法 ,推导了基于高阶循环累积量的模型参数辨识算法 ,具有抑制加性平稳噪声的优点。最后在某型直升机齿轮裂纹故障早期检测中进行了应用 ,结果表明该方法具有很好的预测能力 ,利用模型残差的峭度能够检测和预报早期裂纹故障 ,同时为根据正常样本检测旋转机械故障提供了一种思路Abstract: On the basis of gear vibration signals analysis,one novel method of detecting faults based on linear periodical time-varying autoregressive (LPTV-AR) model according to its cyclostationarity was proposed.The algorithm of identifying model parameters using higher-order cyclic-cumulants was established,which possessed the ability to suppress additive stationary noise.The proposed method was used to detect and predict the incipient gear crack fault of a helicopter.The results show that the approach has good performance of prediction and can detect early gear crack fault using the kurtosis of residual signals.At the same time it provides an idea of detecting faults of rotating machinery using only normal samples.
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