Multidimensional Blind Deconvolution Method Based on Cross-Sparse Filtering for Weak Fault Diagnosis

Abstract

Fault diagnosis and condition monitoring of rotating machinery has drawn considerable attention. The complex structure of rotating machinery and poor working conditions cause two challenges: weak signature detection (WSD) and weak compound fault separation (WCFS). A superior method should realize these two functions simultaneously. This paper proposed a multidimensional blind deconvolution method based on cross-sparse filtering (Cr-SF) for WSD and WCFS, which can enhance the weak signature and decompose the different components from compound fault adaptively without any preprocessing and priori knowledge. Cross kurtosis pursuit (CKP), a novel filter selection technology, is proposed for determining the final filters. The experimental and simulated signals verified the performance of the proposed algorithm. The robustness is also investigated using the success rate of repeated experiments. The results indicate that Cr-SF can handle different fault compounding modes under the strong noise environment and perform strong robustness and noise adaptability.

Keywords

• Unsupervised learning
• cross-sparse filtering
• blind deconvolution
• weak signature detection
• compound fault separation