Rolling element bearings are important components in various types of industrial equipment. It is... more Rolling element bearings are important components in various types of industrial equipment. It is necessary to develop advanced fault diagnosis techniques to prevent unexpected accidents caused by bearing failures. However, impulsive background noise in industrial fields also presents a similar fault-excited characteristic, which brings interference to the fault diagnosis of rolling element bearings. Focusing on this issue, this paper proposes a new feature extraction method based on the cyclic correntropy spectrum (CCES) for intelligent fault identification.. In this study, the cyclic correntropy (CCE) function is introduced to suppress the impulsive noise. A simplified frequency spectrum named CCES is obtained for the feature extraction. Then, narrowband kurtosis vectors are extracted from the CCES. Finally, these extracted features are used to train the least squares support vector machine (LSSVM) for the fault pattern identification. Analyses of two bearing datasets, including train axle bearing data that are contaminated by impulsive noise are used as case studies for the validation of the proposed method. To illustrate the advancement of the new method, performance comparisons with two recently developed methods are conducted. The experimental results verify that the proposed method not only outperforms these two methods but also exhibits a stable self-adaptation ability.
In order to quantitatively and systematically explain the accident occur process and assess the r... more In order to quantitatively and systematically explain the accident occur process and assess the risk for the complex system, this paper proposes a new accident-causing analysis model, i.e. perturbation-safety region (P-SR) model. In this model, the safety region definition is introduced for the quantitative description of the system safe status; also the change process of the system risk is analyzed. The four relative parts included in this model are described in details, such as the risk resource part, the perturbation part, the alarm and system change part, and the accident part. Finally, the proposed model is applied to railway transportation system, and the Wenzhou train collision is systematically analyzed, also the specified control measure for the train emergency dispatch is demonstrated.
The Open Cybernetics & Systemics Journal, Jun 26, 2015
Aim at the modeling problem for distribution service network (DSN) of comprehensive passenger tra... more Aim at the modeling problem for distribution service network (DSN) of comprehensive passenger transport hub (CPTH), the three-phrase modeling method for designing DSN is proposed, which consists of factor analysis, factor description and network integration. Based on the analysis to passenger transport behavior demand, the conception of DSN is defined for the first time. The DSN is a network system possesses of spatial-temporal feature and distribution function, which is coupled by three parts such as internal passenger flow distribution infrastructure, passenger organization process and guidance service facilities. Using the theory and method of system engineering to study on the properties of three layers, the property description model for each layer is established. By integrating space attribute data and service attribute data of each layer, the three-phrase modeling method and operation process are put forward, therewith, the model of DSN is built, ultimately realizes the digital description of DSN. The case shows that this method has strong readability and operability.
This paper mainly discusses the performance degradation assessment of train rolling bearings unde... more This paper mainly discusses the performance degradation assessment of train rolling bearings under incomplete data, by using the support vector data description (SVDD) and dynamic particle swarm optimization (DPSO).The proposed method is based on the similarity weight for the assessment of the train rolling bearings under incomplete data. Firstly, to obtain effective features of bearing performance degradation from collected vibration data, the local mean decomposition (LMD) is employed to decompose the vibration data. Secondly, the high-dimensionality of features is reduced by the principal component analysis (PCA). And then, on the basis of choosing the kernel parameter and penalty weight, a degradation method based on SVDD is proposed. Finally, the experimental results verified that the proposed method has a better optimization performance than the traditional method and can assess the performance degradation of train rolling bearings under incomplete data.
Roller bearing plays a significant role in industrial sectors. To improve the ability of roller b... more Roller bearing plays a significant role in industrial sectors. To improve the ability of roller bearing fault diagnosis under multi-rotating situation, this paper proposes a novel roller bearing fault characteristic: the Amplitude Modulation (AM) based correntropy extracted from the Intrinsic Mode Functions (IMFs), which are decomposed by Fast Ensemble Empirical mode decomposition (FEEMD) and employ Least Square Support Vector Machine (LSSVM) to implement intelligent fault identification. Firstly, the roller bearing vibration acceleration signal is decomposed by FEEMD to extract IMFs. Secondly, IMF correntropy matrix (IMFCM) as the fault feature matrix is calculated from the AM-correntropy model of the primary vibration signal and IMFs. Furthermore, depending on LSSVM, the fault identification results of the roller bearing are obtained. Through the bearing identification experiments in stationary rotating conditions, it was verified that IMFCM generates more stable and higher diagnosis accuracy than conventional fault features such as energy moment, fuzzy entropy, and spectral kurtosis. Additionally, it proves that IMFCM has more diagnosis robustness than conventional fault features under cross-mixed roller bearing operating conditions. The diagnosis accuracy was more than 84% for the cross-mixed operating condition, which is much higher than the traditional features. In conclusion, it was proven that FEEMD-IMFCM-LSSVM is a reliable technology for roller bearing fault diagnosis under the constant or multi-positioned operating conditions, and as such, it possesses potential prospects for a broad application of uses.
Speed profile optimization plays an important role in optimal train control. Considering the char... more Speed profile optimization plays an important role in optimal train control. Considering the characteristics of an electrical locomotive with regenerative braking, this paper proposes a new algorithm for target speed profile approximation. This paper makes the following three contributions: First, it proves that under a certain calculation precision, there is an optimal coast-brake switching region-not just a point where the train should be switched from coasting mode to braking mode. This is very useful in engineering applications. Second, the paper analyzes the influence of regenerative braking on the optimal coast-brake switching region and proposes an approximate linear relationship between the optimal switching region and the regeneration efficiency. Third, the paper presents an average speed equivalent algorithm for local speed profile optimization in steep sections. In addition, this paper simplifies the proof of the optimality of smooth running on a non-steep track in two steps. The effects on energy consumption from two important factors (optimal coast and running time) are systematically analyzed. Extensive simulations verify these points of view and demonstrate that the proposed approximation approach is computationally efficient and achieves sufficient engineering accuracy.
A train traction system is an important part of an urban rail transit system. However, a train tr... more A train traction system is an important part of an urban rail transit system. However, a train traction system has many components and a high risk of internal faults. How to systematically evaluate the fault coverage and diagnosis ability of testing equipment is a fundamental problem in the technical field of train operation. In response to this problem, this study attempts to apply testability technology to the test capability analysis of train traction systems for rail transit. In view of the uncertainty in actual tests, a method for constructing a fault diagnosis strategy for a traction system under unreliable testing is proposed. The concept of test credibility is introduced for the first time, and the quantitative evaluation of test credibility is realized using a cloud model, so as to construct a new “fault-test” credibility correlation matrix. On this basis, a single-fault diagnosis strategy of the traction system is constructed and compared based on information theory. The r...
The condition monitoring of railway track line is one of the essential tasks to ensure the safety... more The condition monitoring of railway track line is one of the essential tasks to ensure the safety of the railway transportation system. Railway track line is mainly composed of tracks, fasteners, sleepers, and so on. Given the requirements for rapid and accurate inspection, innovative and intelligent methods for multi-target defect identification of the railway track line using image processing and deep learning methods are proposed in this paper. Firstly, the track and fastener positioning method based on variance projection and wavelet transform is introduced. After that, a bag-of-visual-word (BOVW) model combined with spatial pyramid decomposition is proposed for railway track line multi-target defect detection with a detection accuracy of 96.26%. Secondly, an improved YOLOv3 model named TLMDDNet (Track Line Multi-target Defect Detection Network), integrating scale reduction and feature concatenation, is proposed to enhance detection accuracy and efficiency. Finally, to reduce model complexity and further improve the detection speed, with the help of dense connection structure, a lightweight design strategy for the TLMDDNet model named DC-TLMDDNet (Dense Connection Based TLMDDNet) is proposed, in which the DenseNet is applied to optimize feature extraction layers in the backbone network of TLMDDNet. The effectiveness of the proposed methods is demonstrated by the experimental results. INDEX TERMS Railway track line defects, multi-target defect identification, image processing, deep learning, YOLOv3. XIUKUN WEI received the Ph.D. degree
Zenodo (CERN European Organization for Nuclear Research), Aug 29, 2010
This paper analysis the integrated use of safety monitoring with the domestic and international l... more This paper analysis the integrated use of safety monitoring with the domestic and international latest research on rail safety protection system, and focus on the implementation of an organic whole system, with the monitoring and early warning, risk assessment, predictive control and emergency rescue system. The system framework, contents and system structure of Security system is proposed completely. It's pointed out that the Security system is a negative feedback system composed of by safety monitoring and warning system, risk assessment and emergency rescue system. Safety monitoring and warning system focus on the monitoring target monitoring, early warning, tracking, integration of decision-making, for objective and subjective risks factors. Risk assessment system analysis the occurrence of a major Security risk mechanism, determines the standard of the future short, medium and long term safety conditions, and give prop for development of safety indicators, accident analysis and safety standards. Emergency rescue system is with the goal of rapid and effective rescue work for accident, to minimize casualties and property losses.
The subway station is an important place for passenger flow distribution in subway networks, and ... more The subway station is an important place for passenger flow distribution in subway networks, and real-time monitoring of passenger flow within stations helps promote the safe and efficient operation of the entire subway network. However, large-scale crowded passenger flow still exist in subway stations, making it challenging to accurately assess the massive passenger flow inside the stations. In this regard, this paper proposes a crowd counting method based on a deep learning framework with a dual attention mechanism. It aims to tackle the problem of counting large crowds within a subway station. This method provides strong support for ensuring passenger safety in subsequent operations. The key components of our proposed model are the multi-scale attention module and the deformable attention module. The multi-scale attention module can effectively extract multi-scale features of crowds and extract informative features from heavily occluded areas while ensuring that the channel count and resolution of the input feature map remain unchanged. In the Transformer framework, the deformable attention module dynamically assigns attention weights to each feature position, enabling a more suitable crowd counting model for congested conditions in subway station. Three commonly used benchmark datasets for crowd counting were used in a wide range of experiments. The experimental results demonstrate that our model achieves relatively good performance compared to existing popular algorithms. Additionally, existing crowd counting datasets do not adequately capture the variations in multi-scale and crowd occlusion scenarios specific to subway station environments. Therefore, this paper constructed a custom dense crowd dataset for subway station platforms. Our method performs better on this self-built dataset, which focuses on capturing the challenges of multi-scale variations and crowd occlusions in subway station scenarios, as demonstrated by experimental results.
The health condition of a wheelset bearing, the key component of a railway bogie, has a considera... more The health condition of a wheelset bearing, the key component of a railway bogie, has a considerable impact on the safety of a train. Traditional bearing fault diagnosis techniques generally extract signals manually and then diagnose the bearing health conditions through the classifier. However, high-speed trains (HSTs) are usually faced with variable loads, variable speeds, and strong environmental noise, which pose a huge challenge to the application of the traditional bearing fault diagnosis methods in wheelset bearing fault diagnosis. Therefore, this paper proposes a 1D residual block, and based on the block, a novel deeper 1D convolutional neural network (Der-1DCNN) is proposed. The framework includes the idea of residual learning and can effectively learn high-level and abstract features while effectively alleviating the problem of training difficulty and the performance degradation of a deeper network. Additionally, for the first time, we fully use the wide convolution kernel and dropout technology to improve the model's ability to learn low-frequency signal features related to the fault components and to enhance the network's generalization performance. By constructing a deep residual learning network, Der-1DCNN can adaptively learn the deep fault features of the original vibration signal. This method not only achieves very high diagnostic accuracy for the fault diagnosis task of wheelset bearings in HSTs under strong noise environment, but also its performance is quite superior when the train's working load changes without any domain adaptation algorithm processing. The proposed Der-1DCNN is evaluated on the dataset of the multi-operating conditions of the wheelset bearings of HSTs. Experiments show that this method shows a better diagnostic performance compared with the state-of-the-art deep learning methods of bearing fault diagnosis, which proves the method's effectiveness and superiority. INDEX TERMS High-speed trains, wheelset bearings fault diagnosis, deep learning, one-dimensional residual block, wide convolutional kernel.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
In view of the negative impact of component importance measures based on system reliability theor... more In view of the negative impact of component importance measures based on system reliability theory and centrality measures based on complex networks theory, there is an attempt to provide improved centrality measures (ICMs) construction method with fuzzy integral for measuring the importance of components in electromechanical systems in this paper. ICMs are the meaningful extension of centrality measures and component importance measures, which consider influences on function and topology between components to increase importance measures usefulness. Our work makes two important contributions. First, we propose a novel integration method of component importance measures to define ICMs based on Choquet integral. Second, a meaningful fuzzy integral is first brought into the construction comprehensive measure by fusion multi-ICMs and then identification of important components which could give consideration to the function of components and topological structure of the whole system. In addition, the construction method of ICMs and comprehensive measure by integration multi-CIMs based on fuzzy integral are illustrated with a holistic topological network of bogie system that consists of 35 components.
COVID-19 spreads rapidly among people, so that more and more people are wearing masks in rail tra... more COVID-19 spreads rapidly among people, so that more and more people are wearing masks in rail transit stations. However, the current face detection algorithms cannot distinguish between a face wearing a mask and a face not wearing a mask. This paper proposes a face detection algorithm based on single shot detector and active learning in rail transit surveillance, effectively detecting faces and faces wearing masks. Firstly, we propose a real-time face detection algorithm based on single shot detector, which improves the accuracy by optimizing backbone network, feature pyramid network, spatial attention module, and loss function. Subsequently, this paper proposes a semi-supervised active learning method to select valuable samples from video surveillance of rail transit to retrain the face detection algorithm, which improves the generalization of the algorithm in rail transit and reduces the time to label samples. Extensive experimental results demonstrate that the proposed method achieves significant performance over the state-of-the-art algorithms on rail transit dataset. The proposed algorithm has a wide range of applications in rail transit stations, including passenger flow statistics, epidemiological analysis, and reminders of passenger who do not wear masks. Simultaneously, our algorithm does not collect and store face information of passengers, which effectively protects the privacy of passengers.
The state monitoring of the railway track line is one of the important tasks to ensure the safety... more The state monitoring of the railway track line is one of the important tasks to ensure the safety of the railway transportation system. While the defect recognition result, that is, the inspection report, is the main basis for the maintenance decision. Most previous attempts have proposed intelligent detection methods to achieve rapid and accurate inspection of the safety state of the railway track line. However, there are few investigations on the automatic generation of inspection reports. Fortunately, inspired by the recent advances and successes in dense captioning, such technologies can be investigated and used to generate textual information on the type, position, status, and interrelationship of the key components from the field images. To this end, based on the work of DenseCap, a railway track line image captioning model (RTLCap for short) is proposed, which replaces VGG16 with ResNet-50-FPN as the backbone of the model to extract more powerful image features. In addition, ...
Purpose This paper aims to define the concept, composition, connotation, functional technology an... more Purpose This paper aims to define the concept, composition, connotation, functional technology and development path of autonomous transportation systems (ATS) and provide theoretical basis and support for the construction and development of ATS. Design/methodology/approach The research analyzes the concept and connotation of ATS, studies the composition and structure of ATS, sorts out pillar function technology system including perception, digitization, interoperability, computing and integration in ATS hierarchically, and looks forward to the future development path of ATS from human participation and systems intelligence. Findings This paper puts forward the concept, composition, connotation and structure of ATS, proposes the pillar functional technology system of ATS and proposes four development stages of ATS. Originality/value The research can provide a theoretical and scientific basis for the high-quality, efficient, orderly construction and development of ATS.
With the increase of the railway operating mileage, the railway network is becoming more and more... more With the increase of the railway operating mileage, the railway network is becoming more and more complicated. We expect to build more railway lines to offer the possibility to offer more high quality service for the passengers, while the investment is often limited. Therefore, it is very important to decide the pairs of cities to add new railway lines under the condition of limited construction investment in order to optimize the railway line network to maximize the reliability of the railway network to deal with the railway passenger transport task under emergency conditions. In this paper, we firstly define the reliability of the railway networks based on probability theory by analyzing three minor cases. Then we construct a reliability optimization model for the railway network to solve the problem, expecting to enhance the railway network with the limited investment. The goal is to make an optimal decision when choosing where to add new railway lines to maximize the reliability of the whole railway network, taking the construction investment as the main constraint, which is turned to the building mileage limit. A computing case is presented based on the railway network of Shandong Province, China. The computing results prove the effectiveness of the model and the efficiency of the algorithm. The approach presented in this paper can provide a reference for the railway investors and builders to make an optimal decision.
Accurate estimated capacity of the railway section can provide reliable information to railway op... more Accurate estimated capacity of the railway section can provide reliable information to railway operators and engineers in decision-making, particularly, in an emergency situation. However, in an emergency, the optimization of capacity of a railway section is usually involved to study, for example, the characteristics of dynamic, fuzziness, randomness, and non-aftereffect properties. This paper presents a proposed capacity calculation method based on the modified fuzzy Markov chain (MFMC). In this method, the capacity of a railway section in an emergency can be expressed by a fuzzy random variable, which remains the randomness of capacity changing according to the impact of emergencies and the fuzziness of the driving behavior and other factors. A case study of a high-speed line from Beijing to Shanghai is used to show the process of the proposed methods for optimization of section capacity calculation in an emergency.
In order to be environment-friendly, relieve traffic congestion, reduce pollution, and be green a... more In order to be environment-friendly, relieve traffic congestion, reduce pollution, and be green and sustainable, the optimization and development of public transportation, as the subject of people's long-term research, has always been shining. With the emergence of shared transportation, public transportation systems face more challenges. In order to better connect with bike-sharing, car-sharing, and other modes of transportation, public transportation will carry out important reforms, among which the optimization of line network is one of the most important tasks. The traditional bus route design is mainly based on the “four-stage” method model, which is mainly based on the investigation and analysis of the existing traffic system and land use. Through the work flow of “evaluation, calibration, and verification,” the network balance optimization model is used to get the bus travel allocation prediction model. In this paper, the optimization problem of public transit network is ...
Purpose Green energy as a transportation supply trend is irreversible. In this paper, a highway e... more Purpose Green energy as a transportation supply trend is irreversible. In this paper, a highway energy supply system (HESS) evolution model is proposed to provide highway transportation vehicles and service facilities with a clean electricity supply and form a new model of a source-grid-load-storage-charge synergistic highway-PV-WT integrated system (HPWIS). This paper aims to improve the flexibility index of highways and increase CO2 emission reduction of highways. Design/methodology/approach To maximize the integration potential, a new energy-generation, storage and information-integration station is established with a dynamic master–slave game model. The flexibility index is defined to evaluate the system ability to manage random fluctuations in power generation and load levels. Moreover, CO2 emission reduction is also quantified. Finally, the Lianhuo Expressway is taken as an example to calculate emission reduction and flexibility. Findings The results show that through the appl...
Rolling element bearings are important components in various types of industrial equipment. It is... more Rolling element bearings are important components in various types of industrial equipment. It is necessary to develop advanced fault diagnosis techniques to prevent unexpected accidents caused by bearing failures. However, impulsive background noise in industrial fields also presents a similar fault-excited characteristic, which brings interference to the fault diagnosis of rolling element bearings. Focusing on this issue, this paper proposes a new feature extraction method based on the cyclic correntropy spectrum (CCES) for intelligent fault identification.. In this study, the cyclic correntropy (CCE) function is introduced to suppress the impulsive noise. A simplified frequency spectrum named CCES is obtained for the feature extraction. Then, narrowband kurtosis vectors are extracted from the CCES. Finally, these extracted features are used to train the least squares support vector machine (LSSVM) for the fault pattern identification. Analyses of two bearing datasets, including train axle bearing data that are contaminated by impulsive noise are used as case studies for the validation of the proposed method. To illustrate the advancement of the new method, performance comparisons with two recently developed methods are conducted. The experimental results verify that the proposed method not only outperforms these two methods but also exhibits a stable self-adaptation ability.
In order to quantitatively and systematically explain the accident occur process and assess the r... more In order to quantitatively and systematically explain the accident occur process and assess the risk for the complex system, this paper proposes a new accident-causing analysis model, i.e. perturbation-safety region (P-SR) model. In this model, the safety region definition is introduced for the quantitative description of the system safe status; also the change process of the system risk is analyzed. The four relative parts included in this model are described in details, such as the risk resource part, the perturbation part, the alarm and system change part, and the accident part. Finally, the proposed model is applied to railway transportation system, and the Wenzhou train collision is systematically analyzed, also the specified control measure for the train emergency dispatch is demonstrated.
The Open Cybernetics & Systemics Journal, Jun 26, 2015
Aim at the modeling problem for distribution service network (DSN) of comprehensive passenger tra... more Aim at the modeling problem for distribution service network (DSN) of comprehensive passenger transport hub (CPTH), the three-phrase modeling method for designing DSN is proposed, which consists of factor analysis, factor description and network integration. Based on the analysis to passenger transport behavior demand, the conception of DSN is defined for the first time. The DSN is a network system possesses of spatial-temporal feature and distribution function, which is coupled by three parts such as internal passenger flow distribution infrastructure, passenger organization process and guidance service facilities. Using the theory and method of system engineering to study on the properties of three layers, the property description model for each layer is established. By integrating space attribute data and service attribute data of each layer, the three-phrase modeling method and operation process are put forward, therewith, the model of DSN is built, ultimately realizes the digital description of DSN. The case shows that this method has strong readability and operability.
This paper mainly discusses the performance degradation assessment of train rolling bearings unde... more This paper mainly discusses the performance degradation assessment of train rolling bearings under incomplete data, by using the support vector data description (SVDD) and dynamic particle swarm optimization (DPSO).The proposed method is based on the similarity weight for the assessment of the train rolling bearings under incomplete data. Firstly, to obtain effective features of bearing performance degradation from collected vibration data, the local mean decomposition (LMD) is employed to decompose the vibration data. Secondly, the high-dimensionality of features is reduced by the principal component analysis (PCA). And then, on the basis of choosing the kernel parameter and penalty weight, a degradation method based on SVDD is proposed. Finally, the experimental results verified that the proposed method has a better optimization performance than the traditional method and can assess the performance degradation of train rolling bearings under incomplete data.
Roller bearing plays a significant role in industrial sectors. To improve the ability of roller b... more Roller bearing plays a significant role in industrial sectors. To improve the ability of roller bearing fault diagnosis under multi-rotating situation, this paper proposes a novel roller bearing fault characteristic: the Amplitude Modulation (AM) based correntropy extracted from the Intrinsic Mode Functions (IMFs), which are decomposed by Fast Ensemble Empirical mode decomposition (FEEMD) and employ Least Square Support Vector Machine (LSSVM) to implement intelligent fault identification. Firstly, the roller bearing vibration acceleration signal is decomposed by FEEMD to extract IMFs. Secondly, IMF correntropy matrix (IMFCM) as the fault feature matrix is calculated from the AM-correntropy model of the primary vibration signal and IMFs. Furthermore, depending on LSSVM, the fault identification results of the roller bearing are obtained. Through the bearing identification experiments in stationary rotating conditions, it was verified that IMFCM generates more stable and higher diagnosis accuracy than conventional fault features such as energy moment, fuzzy entropy, and spectral kurtosis. Additionally, it proves that IMFCM has more diagnosis robustness than conventional fault features under cross-mixed roller bearing operating conditions. The diagnosis accuracy was more than 84% for the cross-mixed operating condition, which is much higher than the traditional features. In conclusion, it was proven that FEEMD-IMFCM-LSSVM is a reliable technology for roller bearing fault diagnosis under the constant or multi-positioned operating conditions, and as such, it possesses potential prospects for a broad application of uses.
Speed profile optimization plays an important role in optimal train control. Considering the char... more Speed profile optimization plays an important role in optimal train control. Considering the characteristics of an electrical locomotive with regenerative braking, this paper proposes a new algorithm for target speed profile approximation. This paper makes the following three contributions: First, it proves that under a certain calculation precision, there is an optimal coast-brake switching region-not just a point where the train should be switched from coasting mode to braking mode. This is very useful in engineering applications. Second, the paper analyzes the influence of regenerative braking on the optimal coast-brake switching region and proposes an approximate linear relationship between the optimal switching region and the regeneration efficiency. Third, the paper presents an average speed equivalent algorithm for local speed profile optimization in steep sections. In addition, this paper simplifies the proof of the optimality of smooth running on a non-steep track in two steps. The effects on energy consumption from two important factors (optimal coast and running time) are systematically analyzed. Extensive simulations verify these points of view and demonstrate that the proposed approximation approach is computationally efficient and achieves sufficient engineering accuracy.
A train traction system is an important part of an urban rail transit system. However, a train tr... more A train traction system is an important part of an urban rail transit system. However, a train traction system has many components and a high risk of internal faults. How to systematically evaluate the fault coverage and diagnosis ability of testing equipment is a fundamental problem in the technical field of train operation. In response to this problem, this study attempts to apply testability technology to the test capability analysis of train traction systems for rail transit. In view of the uncertainty in actual tests, a method for constructing a fault diagnosis strategy for a traction system under unreliable testing is proposed. The concept of test credibility is introduced for the first time, and the quantitative evaluation of test credibility is realized using a cloud model, so as to construct a new “fault-test” credibility correlation matrix. On this basis, a single-fault diagnosis strategy of the traction system is constructed and compared based on information theory. The r...
The condition monitoring of railway track line is one of the essential tasks to ensure the safety... more The condition monitoring of railway track line is one of the essential tasks to ensure the safety of the railway transportation system. Railway track line is mainly composed of tracks, fasteners, sleepers, and so on. Given the requirements for rapid and accurate inspection, innovative and intelligent methods for multi-target defect identification of the railway track line using image processing and deep learning methods are proposed in this paper. Firstly, the track and fastener positioning method based on variance projection and wavelet transform is introduced. After that, a bag-of-visual-word (BOVW) model combined with spatial pyramid decomposition is proposed for railway track line multi-target defect detection with a detection accuracy of 96.26%. Secondly, an improved YOLOv3 model named TLMDDNet (Track Line Multi-target Defect Detection Network), integrating scale reduction and feature concatenation, is proposed to enhance detection accuracy and efficiency. Finally, to reduce model complexity and further improve the detection speed, with the help of dense connection structure, a lightweight design strategy for the TLMDDNet model named DC-TLMDDNet (Dense Connection Based TLMDDNet) is proposed, in which the DenseNet is applied to optimize feature extraction layers in the backbone network of TLMDDNet. The effectiveness of the proposed methods is demonstrated by the experimental results. INDEX TERMS Railway track line defects, multi-target defect identification, image processing, deep learning, YOLOv3. XIUKUN WEI received the Ph.D. degree
Zenodo (CERN European Organization for Nuclear Research), Aug 29, 2010
This paper analysis the integrated use of safety monitoring with the domestic and international l... more This paper analysis the integrated use of safety monitoring with the domestic and international latest research on rail safety protection system, and focus on the implementation of an organic whole system, with the monitoring and early warning, risk assessment, predictive control and emergency rescue system. The system framework, contents and system structure of Security system is proposed completely. It's pointed out that the Security system is a negative feedback system composed of by safety monitoring and warning system, risk assessment and emergency rescue system. Safety monitoring and warning system focus on the monitoring target monitoring, early warning, tracking, integration of decision-making, for objective and subjective risks factors. Risk assessment system analysis the occurrence of a major Security risk mechanism, determines the standard of the future short, medium and long term safety conditions, and give prop for development of safety indicators, accident analysis and safety standards. Emergency rescue system is with the goal of rapid and effective rescue work for accident, to minimize casualties and property losses.
The subway station is an important place for passenger flow distribution in subway networks, and ... more The subway station is an important place for passenger flow distribution in subway networks, and real-time monitoring of passenger flow within stations helps promote the safe and efficient operation of the entire subway network. However, large-scale crowded passenger flow still exist in subway stations, making it challenging to accurately assess the massive passenger flow inside the stations. In this regard, this paper proposes a crowd counting method based on a deep learning framework with a dual attention mechanism. It aims to tackle the problem of counting large crowds within a subway station. This method provides strong support for ensuring passenger safety in subsequent operations. The key components of our proposed model are the multi-scale attention module and the deformable attention module. The multi-scale attention module can effectively extract multi-scale features of crowds and extract informative features from heavily occluded areas while ensuring that the channel count and resolution of the input feature map remain unchanged. In the Transformer framework, the deformable attention module dynamically assigns attention weights to each feature position, enabling a more suitable crowd counting model for congested conditions in subway station. Three commonly used benchmark datasets for crowd counting were used in a wide range of experiments. The experimental results demonstrate that our model achieves relatively good performance compared to existing popular algorithms. Additionally, existing crowd counting datasets do not adequately capture the variations in multi-scale and crowd occlusion scenarios specific to subway station environments. Therefore, this paper constructed a custom dense crowd dataset for subway station platforms. Our method performs better on this self-built dataset, which focuses on capturing the challenges of multi-scale variations and crowd occlusions in subway station scenarios, as demonstrated by experimental results.
The health condition of a wheelset bearing, the key component of a railway bogie, has a considera... more The health condition of a wheelset bearing, the key component of a railway bogie, has a considerable impact on the safety of a train. Traditional bearing fault diagnosis techniques generally extract signals manually and then diagnose the bearing health conditions through the classifier. However, high-speed trains (HSTs) are usually faced with variable loads, variable speeds, and strong environmental noise, which pose a huge challenge to the application of the traditional bearing fault diagnosis methods in wheelset bearing fault diagnosis. Therefore, this paper proposes a 1D residual block, and based on the block, a novel deeper 1D convolutional neural network (Der-1DCNN) is proposed. The framework includes the idea of residual learning and can effectively learn high-level and abstract features while effectively alleviating the problem of training difficulty and the performance degradation of a deeper network. Additionally, for the first time, we fully use the wide convolution kernel and dropout technology to improve the model's ability to learn low-frequency signal features related to the fault components and to enhance the network's generalization performance. By constructing a deep residual learning network, Der-1DCNN can adaptively learn the deep fault features of the original vibration signal. This method not only achieves very high diagnostic accuracy for the fault diagnosis task of wheelset bearings in HSTs under strong noise environment, but also its performance is quite superior when the train's working load changes without any domain adaptation algorithm processing. The proposed Der-1DCNN is evaluated on the dataset of the multi-operating conditions of the wheelset bearings of HSTs. Experiments show that this method shows a better diagnostic performance compared with the state-of-the-art deep learning methods of bearing fault diagnosis, which proves the method's effectiveness and superiority. INDEX TERMS High-speed trains, wheelset bearings fault diagnosis, deep learning, one-dimensional residual block, wide convolutional kernel.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
In view of the negative impact of component importance measures based on system reliability theor... more In view of the negative impact of component importance measures based on system reliability theory and centrality measures based on complex networks theory, there is an attempt to provide improved centrality measures (ICMs) construction method with fuzzy integral for measuring the importance of components in electromechanical systems in this paper. ICMs are the meaningful extension of centrality measures and component importance measures, which consider influences on function and topology between components to increase importance measures usefulness. Our work makes two important contributions. First, we propose a novel integration method of component importance measures to define ICMs based on Choquet integral. Second, a meaningful fuzzy integral is first brought into the construction comprehensive measure by fusion multi-ICMs and then identification of important components which could give consideration to the function of components and topological structure of the whole system. In addition, the construction method of ICMs and comprehensive measure by integration multi-CIMs based on fuzzy integral are illustrated with a holistic topological network of bogie system that consists of 35 components.
COVID-19 spreads rapidly among people, so that more and more people are wearing masks in rail tra... more COVID-19 spreads rapidly among people, so that more and more people are wearing masks in rail transit stations. However, the current face detection algorithms cannot distinguish between a face wearing a mask and a face not wearing a mask. This paper proposes a face detection algorithm based on single shot detector and active learning in rail transit surveillance, effectively detecting faces and faces wearing masks. Firstly, we propose a real-time face detection algorithm based on single shot detector, which improves the accuracy by optimizing backbone network, feature pyramid network, spatial attention module, and loss function. Subsequently, this paper proposes a semi-supervised active learning method to select valuable samples from video surveillance of rail transit to retrain the face detection algorithm, which improves the generalization of the algorithm in rail transit and reduces the time to label samples. Extensive experimental results demonstrate that the proposed method achieves significant performance over the state-of-the-art algorithms on rail transit dataset. The proposed algorithm has a wide range of applications in rail transit stations, including passenger flow statistics, epidemiological analysis, and reminders of passenger who do not wear masks. Simultaneously, our algorithm does not collect and store face information of passengers, which effectively protects the privacy of passengers.
The state monitoring of the railway track line is one of the important tasks to ensure the safety... more The state monitoring of the railway track line is one of the important tasks to ensure the safety of the railway transportation system. While the defect recognition result, that is, the inspection report, is the main basis for the maintenance decision. Most previous attempts have proposed intelligent detection methods to achieve rapid and accurate inspection of the safety state of the railway track line. However, there are few investigations on the automatic generation of inspection reports. Fortunately, inspired by the recent advances and successes in dense captioning, such technologies can be investigated and used to generate textual information on the type, position, status, and interrelationship of the key components from the field images. To this end, based on the work of DenseCap, a railway track line image captioning model (RTLCap for short) is proposed, which replaces VGG16 with ResNet-50-FPN as the backbone of the model to extract more powerful image features. In addition, ...
Purpose This paper aims to define the concept, composition, connotation, functional technology an... more Purpose This paper aims to define the concept, composition, connotation, functional technology and development path of autonomous transportation systems (ATS) and provide theoretical basis and support for the construction and development of ATS. Design/methodology/approach The research analyzes the concept and connotation of ATS, studies the composition and structure of ATS, sorts out pillar function technology system including perception, digitization, interoperability, computing and integration in ATS hierarchically, and looks forward to the future development path of ATS from human participation and systems intelligence. Findings This paper puts forward the concept, composition, connotation and structure of ATS, proposes the pillar functional technology system of ATS and proposes four development stages of ATS. Originality/value The research can provide a theoretical and scientific basis for the high-quality, efficient, orderly construction and development of ATS.
With the increase of the railway operating mileage, the railway network is becoming more and more... more With the increase of the railway operating mileage, the railway network is becoming more and more complicated. We expect to build more railway lines to offer the possibility to offer more high quality service for the passengers, while the investment is often limited. Therefore, it is very important to decide the pairs of cities to add new railway lines under the condition of limited construction investment in order to optimize the railway line network to maximize the reliability of the railway network to deal with the railway passenger transport task under emergency conditions. In this paper, we firstly define the reliability of the railway networks based on probability theory by analyzing three minor cases. Then we construct a reliability optimization model for the railway network to solve the problem, expecting to enhance the railway network with the limited investment. The goal is to make an optimal decision when choosing where to add new railway lines to maximize the reliability of the whole railway network, taking the construction investment as the main constraint, which is turned to the building mileage limit. A computing case is presented based on the railway network of Shandong Province, China. The computing results prove the effectiveness of the model and the efficiency of the algorithm. The approach presented in this paper can provide a reference for the railway investors and builders to make an optimal decision.
Accurate estimated capacity of the railway section can provide reliable information to railway op... more Accurate estimated capacity of the railway section can provide reliable information to railway operators and engineers in decision-making, particularly, in an emergency situation. However, in an emergency, the optimization of capacity of a railway section is usually involved to study, for example, the characteristics of dynamic, fuzziness, randomness, and non-aftereffect properties. This paper presents a proposed capacity calculation method based on the modified fuzzy Markov chain (MFMC). In this method, the capacity of a railway section in an emergency can be expressed by a fuzzy random variable, which remains the randomness of capacity changing according to the impact of emergencies and the fuzziness of the driving behavior and other factors. A case study of a high-speed line from Beijing to Shanghai is used to show the process of the proposed methods for optimization of section capacity calculation in an emergency.
In order to be environment-friendly, relieve traffic congestion, reduce pollution, and be green a... more In order to be environment-friendly, relieve traffic congestion, reduce pollution, and be green and sustainable, the optimization and development of public transportation, as the subject of people's long-term research, has always been shining. With the emergence of shared transportation, public transportation systems face more challenges. In order to better connect with bike-sharing, car-sharing, and other modes of transportation, public transportation will carry out important reforms, among which the optimization of line network is one of the most important tasks. The traditional bus route design is mainly based on the “four-stage” method model, which is mainly based on the investigation and analysis of the existing traffic system and land use. Through the work flow of “evaluation, calibration, and verification,” the network balance optimization model is used to get the bus travel allocation prediction model. In this paper, the optimization problem of public transit network is ...
Purpose Green energy as a transportation supply trend is irreversible. In this paper, a highway e... more Purpose Green energy as a transportation supply trend is irreversible. In this paper, a highway energy supply system (HESS) evolution model is proposed to provide highway transportation vehicles and service facilities with a clean electricity supply and form a new model of a source-grid-load-storage-charge synergistic highway-PV-WT integrated system (HPWIS). This paper aims to improve the flexibility index of highways and increase CO2 emission reduction of highways. Design/methodology/approach To maximize the integration potential, a new energy-generation, storage and information-integration station is established with a dynamic master–slave game model. The flexibility index is defined to evaluate the system ability to manage random fluctuations in power generation and load levels. Moreover, CO2 emission reduction is also quantified. Finally, the Lianhuo Expressway is taken as an example to calculate emission reduction and flexibility. Findings The results show that through the appl...
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