Fault feature extraction of a rotor system based on local mean decomposition and Teager energy kurtosis

Feature extraction is the most important step for machine fault diagnosis, but useful features are very difficult to extract from the vibration signals, especially for intelligent fault diagnosis based on data-driven technique. An integral method for fault feature extraction based on local mean decomposition (LMD) and Teager energy kurtosis (TEK) is proposed in this paper. The raw vibration signals are first processed via LMD to produce a group of product functions (PFs). Then, the Teager energies are computed using the derived PFs. Subsequently, each Teager energy data set is directly used to calculate the corresponding TEK. A vibration experiment was performed on a rotor-bearing rig with rub-impact fault to validate the proposed method. The experimental results show that the proposed method can extract different TEKs from the mechanical vibration signals under two different operating conditions. These TEKs can be employed to identify the normal and rub-impact fault conditions and construct a numerical-valued machine fault decision table, which proves that the proposed method is suitable for fault feature extraction of the rotor-bearing system.