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.
Wildfires, also called forest fires, are a common natural disaster that often occur in forests and are difficult to control. Detecting and suppressing them at an early stage, primarily through monitoring smoke and fires, is crucial in reducing losses. Thanks to the efforts of researchers, wildfire detection technology has advanced significantly, from traditional manual monitoring to target detection, sensor detection, and infrared detection. However, the various detection methods still have some issues, including low accuracy, high costs, slow detection speeds and susceptibility to interference.
This paper presents an improved approach to identifying wildfires based on YOLOv7 with CBAM. Our experimental results indicate that our approach achieves a mean Average Precision (mAP) of 95.77%, surpassing VGG-SSD with CBAM by 0.53%, MobileNetv2-SSD with CBAM by 0.37%, and Faster R-CNN with CBAM by 2.1%. Thus, our method offers a highly accurate approach to wildfire identification.
Wildfire, also known as forest fire, is fire that usually occur in forests and are difficult to control. If it could be detected and suppressed at an early stage (mainly smoke and flames), it has important meaning for reducing the loss. With the attention of relevant researchers, wildfire detection technology has become more and more advanced, from traditional manual monitoring to traditional target detection to sensor detection and infrared detection, etc. The various detection methods involved still have problems such as slow detection speed, low accuracy, easy interference and high cost. In this paper, SSD, an advanced target detection method, was chosen from deep learning algorithms. Three independent SSD networks are built with VGG16, MobileNet v2, and EfficientNet b3 as the backbone. The experimental results show that the mAP (mean Average Precision) of VGG16-SSD is 95.34%, which is 4.76% higher than MobileNet v2-SSD and 4.53% higher than EfficientNet b3-SSD. Therefore, VGG16-SSD can effectively detect wildfires in the early stages.
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