The accumulation of defects on wind turbine blade surfaces can lead to irreversible damage,impacting the aero-dynamic performance of the blades.To address the challenge of detecting and quantifying surface defects on ...The accumulation of defects on wind turbine blade surfaces can lead to irreversible damage,impacting the aero-dynamic performance of the blades.To address the challenge of detecting and quantifying surface defects on wind turbine blades,a blade surface defect detection and quantification method based on an improved Deeplabv3+deep learning model is proposed.Firstly,an improved method for wind turbine blade surface defect detection,utilizing Mobilenetv2 as the backbone feature extraction network,is proposed based on an original Deeplabv3+deep learning model to address the issue of limited robustness.Secondly,through integrating the concept of pre-trained weights from transfer learning and implementing a freeze training strategy,significant improvements have been made to enhance both the training speed and model training accuracy of this deep learning model.Finally,based on segmented blade surface defect images,a method for quantifying blade defects is proposed.This method combines image stitching algorithms to achieve overall quantification and risk assessment of the entire blade.Test results show that the improved Deeplabv3+deep learning model reduces training time by approximately 43.03%compared to the original model,while achieving mAP and MIoU values of 96.87%and 96.93%,respectively.Moreover,it demonstrates robustness in detecting different surface defects on blades across different back-grounds.The application of a blade surface defect quantification method enables the precise quantification of dif-ferent defects and facilitates the assessment of risk levels associated with defect measurements across the entire blade.This method enables non-contact,long-distance,high-precision detection and quantification of surface defects on the blades,providing a reference for assessing surface defects on wind turbine blades.展开更多
目的探讨采用头髓钉下移支持内侧皮质植入技术在股骨近端防旋髓内钉(proximal femoral nail anti-rotation,PFNA)固定治疗内侧壁缺损型股骨转子间骨折的临床疗效。方法分析2021年6月至2024年6月中国人民解放军联勤保障部队第九一〇医院...目的探讨采用头髓钉下移支持内侧皮质植入技术在股骨近端防旋髓内钉(proximal femoral nail anti-rotation,PFNA)固定治疗内侧壁缺损型股骨转子间骨折的临床疗效。方法分析2021年6月至2024年6月中国人民解放军联勤保障部队第九一〇医院采用头髓钉下移支持内侧皮质植入技术行PFNA固定治疗26例内侧壁缺损型股骨转子间骨折患者资料,记录手术时间、术中出血量、骨折愈合时间、并发症发生情况和末次随访髋关节功能Harris评分。结果患者均获得随访,随访时间6~12个月。手术时间20~45(32.7±7.9)min,术中出血量100~350(223.1±63.6)m L,骨折愈合时间10~16周,未出现切口感染、骨折不愈合、头髓钉松动、头颈骨折块内翻等并发症,末次随访时髋关节功能Harris评分优良率为92.3%。结论内侧壁缺损型股骨转子间骨折PFNA固定术中采用头髓钉下移支持内侧皮质植入技术有利增强内侧支撑,降低头颈骨折块内翻的风险,该方法操作简单,固定可靠,临床效果满意。展开更多
为实现航空发动机涡轮叶片射线检测自动化、智能化,有效改善传统射线检测费时费力、效率低下等问题,开展基于无监督学习的涡轮叶片X-ray图像缺陷检测方法研究。基于无监督生成对抗网络,提出一种适用于航空发动机涡轮叶片X-ray图像的缺...为实现航空发动机涡轮叶片射线检测自动化、智能化,有效改善传统射线检测费时费力、效率低下等问题,开展基于无监督学习的涡轮叶片X-ray图像缺陷检测方法研究。基于无监督生成对抗网络,提出一种适用于航空发动机涡轮叶片X-ray图像的缺陷检测算法;构建由生成网络、判别网络和附加自编码网络组成的深度卷积生成对抗网络,设计重构损失、判别损失、编码损失及中间编码损失,并利用4种损失的加权之和构造目标函数;利用完好涡轮叶片X-ray图像进行模型训练,基于训练得到的生成网络建立航空发动机涡轮叶片X-ray图像缺陷检测模型。研究了输入图像大小、编码长度和重构损失对缺陷检测模型性能的影响。结果表明:模型在输入图片像素尺寸为128像素×128像素、编码长度为600、重构损失为L2的情况下检测性能最佳,area under curve(AUC)可达到0.911。该缺陷检测算法能够实现实际生产缺陷零漏检的严苛技术指标,但误检率(>62.1%)较大,作为辅助检测手段应用于实际生产可将人工检测效率提高1.6倍。展开更多
基金supported by the National Science Foundation of China(Grant Nos.52068049 and 51908266)the Science Fund for Distinguished Young Scholars of Gansu Province(No.21JR7RA267)Hongliu Outstanding Young Talents Program of Lanzhou University of Technology.
文摘The accumulation of defects on wind turbine blade surfaces can lead to irreversible damage,impacting the aero-dynamic performance of the blades.To address the challenge of detecting and quantifying surface defects on wind turbine blades,a blade surface defect detection and quantification method based on an improved Deeplabv3+deep learning model is proposed.Firstly,an improved method for wind turbine blade surface defect detection,utilizing Mobilenetv2 as the backbone feature extraction network,is proposed based on an original Deeplabv3+deep learning model to address the issue of limited robustness.Secondly,through integrating the concept of pre-trained weights from transfer learning and implementing a freeze training strategy,significant improvements have been made to enhance both the training speed and model training accuracy of this deep learning model.Finally,based on segmented blade surface defect images,a method for quantifying blade defects is proposed.This method combines image stitching algorithms to achieve overall quantification and risk assessment of the entire blade.Test results show that the improved Deeplabv3+deep learning model reduces training time by approximately 43.03%compared to the original model,while achieving mAP and MIoU values of 96.87%and 96.93%,respectively.Moreover,it demonstrates robustness in detecting different surface defects on blades across different back-grounds.The application of a blade surface defect quantification method enables the precise quantification of dif-ferent defects and facilitates the assessment of risk levels associated with defect measurements across the entire blade.This method enables non-contact,long-distance,high-precision detection and quantification of surface defects on the blades,providing a reference for assessing surface defects on wind turbine blades.
文摘目的探讨采用头髓钉下移支持内侧皮质植入技术在股骨近端防旋髓内钉(proximal femoral nail anti-rotation,PFNA)固定治疗内侧壁缺损型股骨转子间骨折的临床疗效。方法分析2021年6月至2024年6月中国人民解放军联勤保障部队第九一〇医院采用头髓钉下移支持内侧皮质植入技术行PFNA固定治疗26例内侧壁缺损型股骨转子间骨折患者资料,记录手术时间、术中出血量、骨折愈合时间、并发症发生情况和末次随访髋关节功能Harris评分。结果患者均获得随访,随访时间6~12个月。手术时间20~45(32.7±7.9)min,术中出血量100~350(223.1±63.6)m L,骨折愈合时间10~16周,未出现切口感染、骨折不愈合、头髓钉松动、头颈骨折块内翻等并发症,末次随访时髋关节功能Harris评分优良率为92.3%。结论内侧壁缺损型股骨转子间骨折PFNA固定术中采用头髓钉下移支持内侧皮质植入技术有利增强内侧支撑,降低头颈骨折块内翻的风险,该方法操作简单,固定可靠,临床效果满意。
文摘为实现航空发动机涡轮叶片射线检测自动化、智能化,有效改善传统射线检测费时费力、效率低下等问题,开展基于无监督学习的涡轮叶片X-ray图像缺陷检测方法研究。基于无监督生成对抗网络,提出一种适用于航空发动机涡轮叶片X-ray图像的缺陷检测算法;构建由生成网络、判别网络和附加自编码网络组成的深度卷积生成对抗网络,设计重构损失、判别损失、编码损失及中间编码损失,并利用4种损失的加权之和构造目标函数;利用完好涡轮叶片X-ray图像进行模型训练,基于训练得到的生成网络建立航空发动机涡轮叶片X-ray图像缺陷检测模型。研究了输入图像大小、编码长度和重构损失对缺陷检测模型性能的影响。结果表明:模型在输入图片像素尺寸为128像素×128像素、编码长度为600、重构损失为L2的情况下检测性能最佳,area under curve(AUC)可达到0.911。该缺陷检测算法能够实现实际生产缺陷零漏检的严苛技术指标,但误检率(>62.1%)较大,作为辅助检测手段应用于实际生产可将人工检测效率提高1.6倍。
