A technical framework of PHM and active maintenance for modern high-speed railway traction power supply systems

Feng, Donglai; Lin, Senshang; He, Zhengyou; Sun, Xianghong

doi:10.1080/23248378.2017.1286954

Cited by 30 publications

(10 citation statements)

References 18 publications

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“…The POMDP is solved using a one-pass algorithm that is based on n-computation through dynamic programming. In (D. Feng, Lin, He, & Sun, 2017), the authors developed a novel framework that combines PHM and active maintenance to better manage the health and maintenance activities of high speed railways' traction power supply system. The proposed framework consists of classical PHM technology in which RUL, reliability and risk are estimated from different data resources.…”

Section: Aerospacementioning

confidence: 99%

Review of Post-Prognostics Decision-Making in Prognostics and Health Management

Bougacha

Varnier

Zerhouni

2021

IJPHM

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Mainly, the prognostics and health management (PHM) process is based on three processes: the data acquisition and health assessment process in which sensors signals are acquired and processed, the diagnostic and prognostic process in which the source of failure is detected and the remaining useful life (RUL) is predicted and finally the decision- making process that refers to the term management in prognostics and health management. This paper reviews in the literature about the different aspects of decision-making in the context of PHM. The selected papers are subject to con- tent assessment and grouped according to the decision type. Additionally, this paper presents a synthesis of the previous works that helps identify new trends and deficiencies in the decision-making process. The synthesis can guide efforts for future work.

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Section: Aerospacementioning

confidence: 99%

Review of Post-Prognostics Decision-Making in Prognostics and Health Management

Bougacha

Varnier

Zerhouni

2021

IJPHM

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show abstract

“…Furthermore, a comprehensive framework was developed for the AI applications in high-speed rail transportation, considering operational efficiency and customer comfort criteria. Along with the aforementioned efforts, many other studies applied various AI-based methods, optimization, simulation, and other operations research techniques to address different decision problems and issues that are related to train operations, including energy and power sources [80][81][82][83][84][85][86][87][88][89][90][91][92][93][94][95][96], train speed control and trajectory control [97][98][99][100][101][102][103][104][105][106], timetable synchronization and optimization [107][108][109][110][111][112][113][114][115][116][117], as well as intelligent maintenance and prognostics [118][119][120][121][122][123]…”

Section: Figure 16 Iiot-enabled Technologies For Rail Transportationmentioning

confidence: 99%

Deployment of Autonomous Trains in Rail Transportation: Current Trends and Existing Challenges

et al. 2021

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Automation is expected to effectively address the growing demand for passenger and freight transportation, safety issues, human errors, and increasing congestion. The growth of autonomous vehicles using the state-of-the-art connected vehicle technologies has paved the way for the development of passenger and freight autonomous trains (ATs), also known as driverless trains. ATs are fully automated trains that are centrally controlled using advanced communication and internet technologies, such as highspeed internet (5G) technology, Internet of Things, dedicated short range communications, digital video detection cameras, and artificial intelligence-based methods. The current study focuses on a detailed up-todate review of the existing trends, technologies, advancements, and challenges in the deployment of ATs with a full automation level in rail transportation. The basic AT features along with the key technologies that are instrumental for the AT deployment and operations are discussed in detail. Furthermore, a comprehensive evaluation of the state-of-the-art research efforts is performed as well with a specific emphasis on the issues associated with the AT deployment, user perception and outlook for ATs, innovative concepts and models that could be used for the AT design, and the AT operations at highway-rail grade crossings. Based on the conducted review, this study determines the main advantages and challenges from the AT deployment. The identified challenges have to be collaboratively addressed by the relevant stakeholders, including railroad companies, researchers, and government representatives, to facilitate the AT development and deployment considering the perspectives of future users and without affecting the safety level.INDEX TERMS Autonomous trains, driverless trains, connected vehicles, autonomous vehicles, automation trends, automation challenges.

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“…In recent years, the high-speed rails (HSRs) of China have developed rapidly, which means that both scale of operation and catenary expand greatly. The traction power supply system (TPSS) of HSRs requires a very high reliability [1][2][3]. Catenary is a key component of the TPSS, but there is no standby catenary in TPSS.…”

Section: Introductionmentioning

confidence: 99%

A fault prediction method for catenary of high-speed rails based on meteorological conditions

Lin

Wang

et al. 2019

J. Mod. Transport.

Self Cite

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Fault frequency of catenary is related to meteorological conditions. In this work, based on the historical data, catenary fault frequency and weather-related fault rate are introduced to analyse the correlation between catenary faults and meteorological conditions, and further the effect of meteorological conditions on catenary operation. Moreover, machine learning is used for catenary fault prediction. As with the single decision tree, only a small number of training samples can be classified correctly by each weak classifier, the AdaBoost algorithm is adopted to adjust the weights of misclassified samples and weak classifiers, and train multiple weak classifiers. Finally, the weak classifiers are combined to construct a strong classifier, with which the final prediction result is obtained. In order to validate the prediction method, an example is provided based on the historical data from a railway bureau of China. The result shows that the mapping relation between meteorological conditions and catenary faults can be established accurately by AdaBoost algorithm. The AdaBoost algorithm can accurately predict a catenary fault if the meteorological conditions are provided.

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A technical framework of PHM and active maintenance for modern high-speed railway traction power supply systems

Cited by 30 publications

References 18 publications

Review of Post-Prognostics Decision-Making in Prognostics and Health Management

Review of Post-Prognostics Decision-Making in Prognostics and Health Management

Deployment of Autonomous Trains in Rail Transportation: Current Trends and Existing Challenges

A fault prediction method for catenary of high-speed rails based on meteorological conditions

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