Rotating machine fault diagnosis based on intrinsic characteristic-scale decomposition

A new method called intrinsic characteristic-scale decomposition (ICD) is proposed in this paper, which is particularly suitable for processing the nonlinear and non-stationary time series. When fault occurs in gearbox and rolling bearing, the measured vibration signals would exactly present non-stationary characteristics. ICD, a new self-adaptive time-frequency analysis method, can decompose the non-stationary signal into a series of product components (PCs). Therefore, it is possible to diagnose gearbox and rolling bearing fault. In this paper, the ICD method is introduced and the decomposition performance is compared with LMD method. The results demonstrate that ICD has the advantages at least in running time, alleviating the mode mixing problem and restraining the end effect. The ICD method is applied to the practical gear and rolling bearing fault diagnosis. The results demonstrate that the proposed method is effective in the fault signature analysis of the rotating machinery.