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Defect feature recognition method of glass fibre-reinforced structure based on visual image analysis 被引量:1
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作者 HUANG Jingde 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2022年第1期61-67,共7页
Glass fibre-reinforced(GFR)structure is extensively used in radome,spoiler and some other equipment.In engineering practice,due to the influence of wear,aging,impact,chemical corrosion of surface structure and other f... Glass fibre-reinforced(GFR)structure is extensively used in radome,spoiler and some other equipment.In engineering practice,due to the influence of wear,aging,impact,chemical corrosion of surface structure and other factors,the internal structure of this kind of structure gradually evolves into a defect state and expands to form defects such as bubbles,scratches,shorts,cracks,cavitation erosion,stains and other defects.These defects have posed a serious threat to the quality and performance of GFR structure.From the propagation process of GFR structure defects,its duration is random and may be very short.Therefore,designing a scientific micro defect intelligent detection system for GFR structure to enhance the maintainability of GFR structure will not only help to reduce emergencies,but also have positive theoretical significance and application value to ensure safe production and operation.Firstly,the defect detection mechanism of GFR structure is discussed,and the defect detection principle and defect area identification method are analyzed.Secondly,the processing process of defect edge signal is discussed,a classifier based on MLP is established,and the algorithm of the classifier is designed.Finally,the effectiveness of this method is proved by real-time monitoring and defect diagnosis of a typical GFR structure.The experimental results show that this method improves the efficiency of defect detection and has high defect feature recognition accuracy,which provides a new idea for the on-line detection of GFR structure defects. 展开更多
关键词 glass fibre-reinforced(gfr)structure multi-layer perceptron(MLP) machine vision defect detection
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