Condition-based shaft fault diagnosis with the empirical mode decomposition method

Aiming at reducing cost and time of repair, condition-based shaft faults diagnosis is considered an efficient strategy for machine tool community. While the shaft with faults is operating, its vibration signals normally indicate non-linear and non-stationary characteristics but Fourier-based approaches have shown limitations for handling this kind of signal. The methodology proposed in this research is to extract the features from shaft-fault-related vibration signals, from which the corresponding fault condition is then effectively identified. With an incorporation of empirical mode decomposition (EMD) method, the model applied in this research embraces some characteristics, such as zero-crossing rate and energy, of intrinsic mode functions (IMFs) to represent the feature of the shaft condition. In the buildup stage a knowledge ware is created from the database compiled from the existing defect models. Finally, a demonstration applying the present methodology with the knowledge ware using the indicator of correlation to testify its effectiveness yields a good agreement.