An automatic diagnosis method for bearing failure of general aviation piston engine with deep learning networks

Bearing failure is one of the common faults in general aviation piston engines, which directly affects the stability and safety of aircraft. The existing diagnostic methods are manual visual fault detection, and the diagnostic accuracy is greatly by worker experience and has low efficiency. To address the aforementioned shortcomings, an automatic diagnosis method based on deep learning networks is proposed to achieve intelligent detection of bearing failure in this paper. The proposed method adopts an improved YOLOv5 model to process photos of bearing bush during engine maintenance to automatically detect bearing faults. For specific dataset features, in order to enhance the ability of image feature extraction and further improve the ability of small target detection, the proposed DC-YOLOv5 in this paper adopts 160*160 detection layers in the network, introduces a coordinate attention mechanism in the backbone part, adds a feature multi-scale fusion structure in the neck part, and uses Alpha-CIoU loss function in the prediction part. Finally, the comparison experiments were conducted using on-site maintenance data. The experimental results show that the mAP of the proposed DC-YOLOv5 reaches 95.42%, which is 3.21% better than the original YOLOv5s. The detection accuracy is considerably improved compared with other mainstream algorithms. The demonstrated effect with the proposed method is sufficient for the maintenance of general aviation piston engines.