The fasteners employed in the railway tracks are susceptible to defects arising from their intricate composition.Foreign objects are frequently observed on the track bed in an open environment.These two types of defec...The fasteners employed in the railway tracks are susceptible to defects arising from their intricate composition.Foreign objects are frequently observed on the track bed in an open environment.These two types of defects pose potential threats to high-speed trains,thus necessitating timely and accurate track inspection.The majority of extant automatic inspection methods are predicated on the utilization of single visible light data,and the efficacy of the algorithmic processes is influenced by complex environments.Furthermore,due to the single information dimension,the detection accuracy of defects in similar,occluded,and small object categories is low.To address the aforementioned issues,this paper proposes a track defect detectionmethod based on dynamicmulti-modal fusion and challenging object enhanced perception.First,in light of the variances in the representation dimensions ofmultimodal information,this paper proposes a dynamic weighted multi-modal feature fusion module.The fused multi-modal features are assigned weights,and thenmultiplied with the extracted single-modal features atmultiple levels,achieving adaptive adjustment of the response degree of fusion features.Second,a novel stepwise multi-scale convolution feature aggregation module is proposed for challenging objects.The proposed method employs depth separable convolution and cross-scale aggregation operations of different receptive fields to enhance feature extraction and reuse,thereby reducing the degree of progressive loss of effective information.The experimental results demonstrate the efficacy of the proposed method in comparison to eight established methods,encompassing both single-modal and multi-modal methods,as evidenced by the extensive findings within the constructed RGBD dataset.展开更多
As an important component of load transfer,various fatigue damages occur in the track as the rail service life and train traffic increase gradually,such as rail corrugation,rail joint damage,uneven thermite welds,rail ...As an important component of load transfer,various fatigue damages occur in the track as the rail service life and train traffic increase gradually,such as rail corrugation,rail joint damage,uneven thermite welds,rail squats fas-tener defects,etc.Real-time recognition of track defects plays a vital role in ensuring the safe and stable operation of rail transit.In this paper,an intelligent and innovative method is proposed to detect the track defects by using axle-box vibration acceleration and deep learning network,and the coexistence of the above-mentioned typical track defects in the track system is considered.Firstly,the dynamic relationship between the track defects(using the example of the fastening defects)and the axle-box vibration acceleration(ABVA)is investigated using the dynamic vehicle-track model.Then,a simulation model for the coupled dynamics of the vehicle and track with different track defects is established,and the wavelet power spectrum(WPS)analysis is performed for the vibra-tion acceleration signals of the axle box to extract the characteristic response.Lastly,using wavelet spectrum photos as input,an automatic detection technique based on the deep convolution neural network(DCNN)is sug-gested to realize the real-time intelligent detection and identification of various track problems.Thefindings demonstrate that the suggested approach achieves a 96.72%classification accuracy.展开更多
Many researchers are committed to improving the diagnosis accuracy and solving the few-shot problem on circuit breakers(CBs).However,the research on the vibration transmission mechanism of the fault is insufficient,wh...Many researchers are committed to improving the diagnosis accuracy and solving the few-shot problem on circuit breakers(CBs).However,the research on the vibration transmission mechanism of the fault is insufficient,which makes it difficult to find the potential design defects of CBs through vibration.This study proposes a quantitative evaluation method of mechanical defects,which can track and quantify mechanical defects caused by faults adaptively.The fault feature tracking based on ResNet-SHAP can locate the fault feature area in the time-frequency domain and generate the feature distribution maps of faults.Then,the feature factor F is defined to represent the energy of the fault feature.By weighted allocation and extracting positive F,the mechanical defect feature maps are formed.After time-frequency space reconstruction and contact travel matching,the mechanical defects are traced.Experiments show that the quantitative evaluation of mechanical defects has a strong action sequence and structural correlation,and is expandable to different structures of CBs.In addition,it is found that fault feature tracking can adaptively find latent fault features and has strong stability.展开更多
基金funded by Beijing Natural Science Foundation,grant number L241078.
文摘The fasteners employed in the railway tracks are susceptible to defects arising from their intricate composition.Foreign objects are frequently observed on the track bed in an open environment.These two types of defects pose potential threats to high-speed trains,thus necessitating timely and accurate track inspection.The majority of extant automatic inspection methods are predicated on the utilization of single visible light data,and the efficacy of the algorithmic processes is influenced by complex environments.Furthermore,due to the single information dimension,the detection accuracy of defects in similar,occluded,and small object categories is low.To address the aforementioned issues,this paper proposes a track defect detectionmethod based on dynamicmulti-modal fusion and challenging object enhanced perception.First,in light of the variances in the representation dimensions ofmultimodal information,this paper proposes a dynamic weighted multi-modal feature fusion module.The fused multi-modal features are assigned weights,and thenmultiplied with the extracted single-modal features atmultiple levels,achieving adaptive adjustment of the response degree of fusion features.Second,a novel stepwise multi-scale convolution feature aggregation module is proposed for challenging objects.The proposed method employs depth separable convolution and cross-scale aggregation operations of different receptive fields to enhance feature extraction and reuse,thereby reducing the degree of progressive loss of effective information.The experimental results demonstrate the efficacy of the proposed method in comparison to eight established methods,encompassing both single-modal and multi-modal methods,as evidenced by the extensive findings within the constructed RGBD dataset.
基金supported by the Doctoral Fund Project(Grant No.X22003Z).
文摘As an important component of load transfer,various fatigue damages occur in the track as the rail service life and train traffic increase gradually,such as rail corrugation,rail joint damage,uneven thermite welds,rail squats fas-tener defects,etc.Real-time recognition of track defects plays a vital role in ensuring the safe and stable operation of rail transit.In this paper,an intelligent and innovative method is proposed to detect the track defects by using axle-box vibration acceleration and deep learning network,and the coexistence of the above-mentioned typical track defects in the track system is considered.Firstly,the dynamic relationship between the track defects(using the example of the fastening defects)and the axle-box vibration acceleration(ABVA)is investigated using the dynamic vehicle-track model.Then,a simulation model for the coupled dynamics of the vehicle and track with different track defects is established,and the wavelet power spectrum(WPS)analysis is performed for the vibra-tion acceleration signals of the axle box to extract the characteristic response.Lastly,using wavelet spectrum photos as input,an automatic detection technique based on the deep convolution neural network(DCNN)is sug-gested to realize the real-time intelligent detection and identification of various track problems.Thefindings demonstrate that the suggested approach achieves a 96.72%classification accuracy.
基金supported by Smart Grid-National Science and Technology Major Project(Grant 2024ZD0802500).
文摘Many researchers are committed to improving the diagnosis accuracy and solving the few-shot problem on circuit breakers(CBs).However,the research on the vibration transmission mechanism of the fault is insufficient,which makes it difficult to find the potential design defects of CBs through vibration.This study proposes a quantitative evaluation method of mechanical defects,which can track and quantify mechanical defects caused by faults adaptively.The fault feature tracking based on ResNet-SHAP can locate the fault feature area in the time-frequency domain and generate the feature distribution maps of faults.Then,the feature factor F is defined to represent the energy of the fault feature.By weighted allocation and extracting positive F,the mechanical defect feature maps are formed.After time-frequency space reconstruction and contact travel matching,the mechanical defects are traced.Experiments show that the quantitative evaluation of mechanical defects has a strong action sequence and structural correlation,and is expandable to different structures of CBs.In addition,it is found that fault feature tracking can adaptively find latent fault features and has strong stability.
