Rolling Element Bearing Diagnosis Using Particle Filter and Kurtogram

doi:10.3901/JME.2017.03.063

Abstract

Abstract:

Fast Kurtogram has a low performance for the signal with low signal-to-noise ratio (SNR). Particle filter is used as preprocessing method to improve the SNR. It can be used to solve the problem about low performance and improve the fault diagnosis accuracy. State function of the vibration signal can be established. The background noise of origin signal can be extracted. State function and background noise are combined together as the observation function. State function together with observation function is used to construct the state space model. Particle filter is used to estimate new sequence for the noise-reduced signal. The optimal analysis band is obtained by using fast Kurtogram for the noise-reduced signal. Fault characteristic frequency can be obtained based on spectrum analysis. Comparison with fast Kurtogram and empirical mode decomposition filter as preprocessing method for fast Kurtogram, it can be concluded that the proposed method has good performance for rolling element bearing pattern recognition.

Key words: Kurtogram, particle filter, state space, rolling element bearing

LI Hongkun, YANG Rui, REN Yuanjie, HE Delu, GUO Bin. Rolling Element Bearing Diagnosis Using Particle Filter and Kurtogram[J]. Journal of Mechanical Engineering, 2017, 53(3): 63-72.

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Rolling Element Bearing Diagnosis Using Particle Filter and Kurtogram

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