Compound Faults Separation Based on Intrinsic Characteristic-Scale Decomposition and Sparse Component Analysis

Abstract

The frequent occurrence of rotating machinery faults seriously affects the operation of the equipment and the production of the enterprise. And it is worth mentioning that there are few single faults in the rotating equipment failure while the multiple faults are the norm, which undoubtedly increases the difficulty for fault diagnosis. Therefore, it is of great essentiality to address compound faults of rotating machinery. A novel compound faults separation method based on intrinsic characteristic-scale decomposition (ICD) was suggested to detect multi-faults in the case of underdetermined blind source separation (UBSS) when traditional diagnosis techniques fail. To achieve UBSS, ICD is utilized to decompose a single observation signal into multiple product components (PCs). Then, sparse representation is used to improve the signal sparsity, guaranteeing the normal operation of the sparse component analysis (SCA) algorithm. In addition, because the compound fault features cannot only be extracted by ICD, the spares-promoted PCs are arranged in the SCA processing to separate the multiple signal. Simulations and experiments based on the proposed method was successfully verified. Meanwhile, the Empirical mode decomposition (EMD)-based independent component analysis (ICA) is utilized as a contrast to verify the effectiveness of suggested method. The results suggest that the proposed method can deal with the multiple signal separation of roller bearing.