China’s wind power industry has grown dramatically in recent years as the country’s focus on clean energy and renewable energy generation has increased. Mechanical fault diagnosis of wind power transmission is a common wind maintenance method. It has recently become a research hotspot in the field of mechanical fault diagnosis as a method of fault identification based on picture attributes. Time-frequency images, on the other hand, are better for fault analysis and fault diagnosis of wind power transmission machinery than time-domain and frequency-domain images because they contain more information about the operation status of the gear. This work proposes and applies an image feature extraction-based fault diagnostic method to the defect diagnosis of wind-driven mechanical gears. The feature extraction suitable for gear and gear box faults is analyzed, and the improved artificial immune algorithm is used for fault identification. Through collecting normal vibration signals and two kinds of fault vibration signals from the gearbox of wind power transmission in a wind farm and extracting image features on the basis of data processing, the improved algorithm is finally applied for fault analysis. The experimental results show that the fault diagnosis rate of the improved real-value negative selection algorithm is obviously improved and can improve the fault diagnosis rate by 5%.
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