Ensemble EMD-Based Automatic Extraction of the Catenary Structure Wavelength From the Pantograph–Catenary Contact Force

Song

et al. 2017

J. Mod. Transport.

Self Cite

The interaction between the catenary and pantograph is one of the most crucial factors that determine the train operation in high-speed railway. The bad state of catenary is able to directly influence the power supply safety of traction power system. In this paper, four aspects on the catenary research of high-speed railway are reviewed in detail, namely the solution methods for catenary equilibrium state, the dynamic modeling methods of catenary, non-contact detection methods of catenary, and the static and dynamic evaluation methods of catenary. In addition, their recent advances are described. For the low solution accuracy of the initial equilibrium state of catenary, the structure finding method with multi-objective constraint and nonlinear finite element procedure are introduced to solve the problem. For the catenary's dynamic modeling, considering the influence of environmental wind on the catenary, environmental wind simulations and wind tunnel tests are used to obtain the aerodynamic coefficients and build the wind field along the catenary for analysis of its wind vibration characteristics. In order to improve the detection accuracy of non-contact detection for the catenary, the deep learning theory and real-time detection algorithms should be adopted in the future. In view of the lack of dynamic assessment method for the catenary, the modern spectrum evaluation, timefrequency analysis, big data technology and their combinations will be the important means for future catenary evaluation.Keywords High-speed railway Á Modeling of catenary Á Simulation of catenary Á Detection of catenary Á Evaluation of catenary

Section: Dynamic State Evaluation Based On Time-frequency Analysismentioning

confidence: 99%

Advances of research on high-speed railway catenary

Song

et al. 2017

J. Mod. Transport.

Self Cite

“…The CSW is defined as the frequency components that are caused by the cyclic parameter variation of the catenary structure and contained in the pantograph-catenary contact force [6]. As the dominant components in contact force, it plays an important role in the evaluation of current collection quality and anomaly detection.…”

Section: The Csw In Pan-head Accelerationmentioning

confidence: 99%

“…In our previous study, the identification of CSWs in contact force is realized based on the ensemble empirical mode decomposition (EEMD) [6]. For the CSWs in pan-head acceleration, considering the interferences caused by the non-CSW part of the signal, some alterations are required to improve the identification results.…”

Section: Identification Of Csws In Pan-head Accelerationmentioning

confidence: 99%

“…2) It is theoretically proven that the pan-head vertical acceleration can reflect the span-passing and dropper-passing frequencies [4], namely the concept of catenary structure wavelengths (CSWs) proposed in [6] that are the dominant and concerned frequencies in the contact force. Hence, the pan-head vertical acceleration can substitute the contact force in similar methodologies for condition monitoring, in which the frequency components are frequently adopted as the indicator of anomalies.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification of the catenary structure wavelength using pantograph head acceleration measurements

Wang

2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

Núñez

et al. 2017

Self Cite

For the condition monitoring of railway catenaries, the potential utilization of pantograph head (pan-head) vertical acceleration instead of pantograph-catenary contact force is discussed in this paper. In order to establish a baseline of the pan-head acceleration before it can be used for health condition monitoring, one of the essential frequency components, namely the catenary structure wavelength (CSW) is studied. Based on insitu measurements and feature analysis of the pan-head acceleration signal, an adaptive signal filtering approach is proposed to realize the identification of the CSWs. Preliminary results suggest that the CSWs contained in the pan-head acceleration can be reliably identified by the proposed filtering approach.

“…Currently, monitoring railway components is conducted using different technologies and methods [3,4]. Among all Wenqiang Liu is with the School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China (e-mail: Liuwq_2009@126.com).…”

Section: Introductionmentioning

confidence: 99%

Virtual Reality and Convolutional Neural Networks for Railway Catenary Support Components Monitoring

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

Núñez

2019

Self Cite

The development of algorithms for detecting failures in railway catenary support components has, among others, one major challenge: data about healthy components are much more abundant than data about defective components. In this paper, virtual reality technology is employed to control the learning environment of convolutional neural networks (CNNs) for the automatic multicamera-based monitoring of catenary support components. First, 3D image data based on drawings and real-life video images are developed. Then, a virtual reality environment for monitoring the catenary support system is created, emulating real-life conditions such as measurement noise and a multicamera train simulation to resemble state-of-the-art monitoring systems. Then, CNNs are used to extract and fuse the features of multicamera images. Experiments are conducted for monitoring the cantilever support connection, both down (CSC-D) and up (CSC-U), and registration arm support connection, both down (RASC-D) and up (RASC-U). Experimental results show that the CNNs trained in the virtual reality environment can capture the most relevant spatial information of the catenary support components. Multicamera image detection based on CNNs detects screw loss for all four components. For CSC-D and RASC-U, normal and pin-loss images are also fully detected. A challenge remains in increasing the pin-loss detection for both CSC-U and RASC-D.