基于小波包-AR谱技术提取柴油发动机曲轴轴承故障特征

Feature Extraction from the Crank-Shaft Bearing Fault of Diesel Engine Based on the Wavelet Packet-Auto Regressive Model Spectrum Technology

利用小波包分解柴油发动机曲轴轴承振动信号,对不同频段的分解系数进行了时域重构,分别对重构的时间序列进行AR(autoregressive)谱分析,实现了对分析对象的故障特征提取.分析结果表明:小波包-AR谱技术能分离多激励源的干扰,有效地提取柴油发动机曲轴轴承故障特征信号;曲轴轴承特征归一化频段为0～0.25,在发动机转速高于1800r/min时更明显;传感器最佳位置是在曲轴轴承正对的发动机两侧或油底壳处.

The paper explores a way of extracting fault features from the vibration signals of multi-vibration sources, making use of the wavelet packet decomposition of the vibrant signal of a diesel engine crank-shaft bearing, thus reconstructs the time series of the wavelet packet decomposition coefficients in different frequency bands, and analyzes the time series by the AR (autoregressive) model spectrum, picks up the fault characteristic signals of the object analyzed. The result shows that the wavelet packet-AR model spectrum can separate the disturbance of multi-bestirring sources and pick up the fault characteristic signals of the diesel engine crank-shaft bearing effectively. The fault feature frequency band of the crank-shaft bearing is 0～0.25, which is more distinct at the engine's rotative speed of above 1 800 r/min. The optimal positions of the vibration sensor should be located on both sides of the crank-shaft bearing or bottom of the engine.