Autoregressive model-based diagnostics for gears and bearings
Autoregressive model-based diagnostics for gears and bearings
复制标题
DOI：
10.1784/insi.2008.50.8.414
复制
发表时间：
2008-08
期刊：
影响因子：
1.1
通讯作者：
W. Wang
中科院分区：
文献类型：
--
作者：
W. Wang
基于自回归（AR）模型的故障诊断方法采用齿轮信号的AR模型作为线性预测滤波器。对于齿轮诊断，同步信号平均值由AR滤波器处理，局部齿轮故障信息包含在预测误差信号（残差信号）中。对于轴承诊断，在角度域中的同步重采样（未平均）信号由AR滤波器处理。非同步轴承故障信号应保留在残差信号中。本文介绍了AR建模方法的理论背景和AR建模方法在齿轮轴承故障诊断中的应用。应用实例检测局部齿轮和轴承故障，如齿轮齿裂纹和轴承滚道剥落，也提出。该方法可方便地应用于直升机主传动齿轮箱和涡轮机发动机等复杂机械系统的故障诊断。
The autoregressive (AR) model-based fault diagnosis approach employs an AR model of the gear signal as a linear prediction filter. For gear diagnosis, the synchronous signal average is processed by the AR filter with the localised gear fault information being contained in the prediction error signal (residual signal). For bearing diagnosis, the synchronously resampled (not averaged) signal in the angle domain is processed by the AR filter. The non-synchronous bearing fault signal should remain in the residual signal. This paper presents the theoretical background of the AR modelling method and the approaches to gear and bearing diagnosis using the AR modelling method. Application examples of detecting localised gear and bearing faults, such as a gear tooth crack and a bearing raceway spall, are also presented. This approach can be readily applied to fault diagnosis for complex mechanical systems such as helicopter main transmission gearboxes and turbine engines.