Abstract

Wind turbine gearboxes, as the key components of current wind power equipment, are crucial hubs connecting the main shaft to the generator. The internal structure and force of wind turbine gearboxes are complex especially when they are working under various conditions and alternating loads, which can easily result in fault. Therefore, condition monitoring and fault diagnosis of wind power gearboxes are very important to ensure the reliability of the wind power equipment operation. Currently, the structure of wind turbine gearboxes is mainly composed of primary planet transmission and secondary parallel shaft gear transmission. Therefore, once faults occur in planetary gears, transfer paths of the fault vibration signals are time-variant, imposing great challenges to fault diagnosis of wind turbine gearboxes. Meanwhile, due to the influence of transfer paths, the signal sensitivities of diverse measuring points are different, so research on the sensitive measuring points in favor of obtaining fault information is critical to improve the accuracy of fault diagnosis based on vibration signals. Firstly, this paper utilizes the adaptive resonance-based sparse signal decomposition to decompose vibration signals of wind turbine gearboxes, and extracts high-resonance components, low-resonance components and redundant components. The fault feature information obtained from the study is mainly contained in the high-resonance components. Then, the paper uses the relative kurtosis index to analyze and evaluate the high-resonance components of each measuring point. And the concept of relative kurtosis which is used to evaluate the sensitivity of measuring points is proposed. Finally, the locations of sensitive measuring points are determined. The method is applied to the diagnosis of planet carrier bearing outer fault and planetary gear localized spalling fault in a planetary speed-increasing gearbox, which indicates the validity of the research results.

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