Strategy for Detection and Suppression of Random Impulse Interferences in Gearbox Vibration Signals

Vibration monitoring using feature parameters like kurtosis is a key early warning indicator for assessing gearbox health in industrial equipment. However, gearboxes in heavy industries, such as port cranes and steel rolling mills, face complex operating conditions with frequent reversing, start -stop cycles, and random external impacts. The vibration signals from these gearboxes contain considerable random impulse interference, impacting the accuracy of condition monitoring based on feature parameters. To address this issue, a strategy for detection and suppression of random impulse interference is proposed. First, the L-kurtosis and variance-weighted interquartile range (LKVIQR) impulse detection indicator is constructed, and then the random impulse interference detection method based on the ratio of short-time statistical characteristics to long-time statistical characteristics (STSC/LTSC) is proposed. Further, a morphological top-hat averaging operation and a method for determining the length parameter of structural elements (SEs) based on maximizing L-kurtosis are proposed to remove the detected random impulse interference. Finally, the validity of the proposed method is verified through laboratory data and vibration data of the ship unloader in the industrial field. The results indicate that the proposed method effectively reduces the kurtosis index of gearbox vibration signals under random impulse interference, which demonstrate that the proposed method can accurately identify and eliminate random pulse interference in gearbox vibration signals, offering a novel approach to filtering out random impacts during the real-world operational processes of industrial equipment.