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Abstract

The vibration signal containing the fault feature is buried by the strong background noise usually (the noise is referring to the other signals not containing the fault feature in the general sense) when a fault arises in rotating machinery, so the collected vibration signal needs to be processed normally in order to get a correct diagnosis result. Sparse representation theory is a relatively new signal processing method and matching pursuit (MP) is the classical sparse representation method. However, in the traditional MP method only one kind of atom dictionary is constructed to match the fault feature hidden in the vibration signal, which will cause an unsatisfactory analysis result usually when several different kinds of components arise in the vibration signal synchronously. This paper proposes a novel fault feature strengthening method basing on MP to solve the above problem, and the proposed method constructs several different kinds of atom dictionary based on a multi feature pattern set to match the multi-components buried in the collected vibration signal of rotating machinery. Firstly, the effectiveness and advantages of the proposed method are verified by kinds of typical simulation signals of rotating machinery. Then the proposed method is extended to preprocess the vibration signals of bearing and gearbox, and the corresponding analysis results prove that the proposed method has obviously a better fault feature extraction effect on the vibration signal of rotating machinery than the traditional MP method, and the proposed method lays the foundation for further correct fault analysis.

Keywords

Matching pursuit rotating machinery multi feature pattern set feature extraction fault diagnosis

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The application of matching pursuit based on multi feature pattern set in the signal processing of rotating machinery

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References