A Method for Fault Diagnosis of Mechanical Equipment: Iterative Fast Kurtogram

Bearings are the most critical and vulnerable components in rotating machinery. Periodic impulses will be generated when a bearing is operating under a damage condition. Spectral kurtosis (SK) is considered to be an effective tool for impulse detection. The fast Kurtogram (FK) algorithm based on the principle of SK very effective in locating the fault zone, but since the kurtosis value of a single pulse is much larger than that of a periodic pulse, that is, a single strong impact interference will affect the accuracy of the FK algorithm in locating the fault zone. This paper presents an approach called the iterative fast Kurtogram (IFK), which is an enhanced version of the FK algorithm. By integrating an iterative algorithm and statistical thresholds, the IFK method effectively mitigates the impact of a single strong shock on the FK algorithm, contributing to the resolution of the problem at hand. Simulation analysis confirms the effectiveness of the IFK method, highlighting its enhanced robustness compared to the FK method. This improvement contributes to increased accuracy in fault detection.