Monitoring Of Small Damages Of Key Structures Based On Ultrasonic Guided Waves

The development of transportation is one of the key factors to ensure the growth of national economy.At present,there are two typical important equipment in transportation field in China:one is the high-speed train named Fuxing Hao,the other is the large civil aircraft called C919.However,with the development of transportation,traffic accidents often occur due to the structure failures.The bogie frame which is the framework of high-speed train.The bogie frame,especially its welded areas,is one of the most frequent failing parts in high-speed train due to bear complex external force during service.Under bearing complex,long-term fatigue load and unexpected impact load during aircraft flying,the R-zone structure of composite material is very easy to produce fatigue damages,which will influence the structure safety and reduce the service life.Aiming at the two key structures of metal bogie and the R-zone structure of composite material,a small-damage monitoring technique based on ultrasonic guide waves is developed in this thesis.Through utilizing multiple feature extracting methods based on signal energy and root-mean-square deviation,a damage feature extraction method by fusing conventional damage indices is proposed.The technique is utilized to monitor the small-damage and damage growth of two kinds of objects above.The main work of this thesis is as follows:(1)The force of crossbeam and side beam in the bogie frame under high vibration is analyzed,and the welded area of bogie frame is simplified as a T-shape welded test specimen.The characteristics and load of the R-zone structure of the key composite components is analyzed,and R-zone composite test specimens are made.(2)The sensor networks for both T-shape welded test specimens and R-zone composite test specimens are designed and optimized.And the sensor networks are arranged and manufactured by SMART layer technology in order to improve efficient and sensor reliability.(3)Signal feature extraction methods based on energy and root-mean-square deviation are utilized to extract the damage features of both T-shape welded test specimens and R-zone composite test specimens.On the basis of the energy-based features,a new damage factor fusion method is proposed to characterize the damage and its growth.Compared with the traditional method,the proposed method can reduce false negatives or false alarm in the blind area,and obtain better accuracy and wider monitoring range.(4)The proposed method is utilized to monitor the small damages in both T-shape welded test specimens and R-zone composite test specimens to demonstrate its damage detection feasibility.The calibration technique for the quantitative monitoring of the fatigue damage of T-shaped welding test specimens and R-zone composite test specimens are made by the damage identification algorithm.The results show that the 2 mm-length damage and its growth in both types of specimens can be detected by the proposed method.