Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion

Minea, Marius; Dumitrescu, Cătălin; Dima, Mihai

doi:10.3390/s21206876

Cited by 4 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Its core idea is that if a word appears frequently in one document and low in other documents, it indicates that the word has high recognition in the document and assigns its high weight. The feature extraction of signal equipment fault text first needs to realize Chinese word segmentation [26][27][28][29]. Because the high-speed railway signal equipment fault text data contains professional words such as switch machine, red light band and sealer, this paper constructs railway signal professional thesaurus and loads the thesaurus into Jieba word segmentation tool to realize the accurate word segmentation of fault text.…”

Section: Fault Text Feature Representation Of High-speed Railway Sign...mentioning

confidence: 99%

Application and Study of Artificial Intelligence in Railway Signal Interlocking Fault

Lian

Wang

Sharma

et al. 2022

IJCAI

View full text Add to dashboard Cite

The rapid development of railway transportation towards high speed, high density and heavy load has led to even higher requirements for the safety of railway signal equipment. The safety of railway signal equipment is an important part of ensuring railway traffic safety, thus, it is very necessary to study a system that can diagnose the fault of railway signal equipment according to the actual situation. This article utilizes the deep learning algorithm of artificial intelligence for investigating the interlocking faults in the railway transportation. This paper uses ADASYN data synthesis method to synthesize few category samples, uses TF-IDF to extract features and transform vectors, and proposes a deep learning integration method based on combined weight. The results show that BiGRU has better overall classification performance when evaluated on the index of primary and secondary fault classification accuracy. The classification accuracy improvement of 5% is achieved for primary fault classification and the comprehensive evaluation index of secondary fault classification is improved by about 9%. It was revealed that when compared with ADASYN + BiLSTM neural network, the comprehensive evaluation index of primary fault classification accuracy is improved by about 6%, and the comprehensive evaluation index of secondary fault classification is improved by about 10%. It is demonstrated that deep learning integration is an effective method to improve the classification performance of turnout fault diagnosis model.Povzetek: Za železniški sistem je bila uporabljena metodologija globokih nevronskih mrež za iskanje napak v signalih.

show abstract

Section: Fault Text Feature Representation Of High-speed Railway Sign...mentioning

confidence: 99%

Application and Study of Artificial Intelligence in Railway Signal Interlocking Fault

Lian

Wang

Sharma

et al. 2022

IJCAI

View full text Add to dashboard Cite

show abstract

“…However, it still has limitations such as low resolution and an inability to classify targets accurately. To address these limitations, multi-source information fusion theory is applied to integrate the advantages of different types of sensors [9,10]. By acquiring information from multiple 1.…”

Section: Introductionmentioning

confidence: 99%

Study on Multi-Heterogeneous Sensor Data Fusion Method Based on Millimeter-Wave Radar and Camera

Duan

2023

Sensors

View full text Add to dashboard Cite

This study presents a novel multimodal heterogeneous perception cross-fusion framework for intelligent vehicles that combines data from millimeter-wave radar and camera to enhance target tracking accuracy and handle system uncertainties. The framework employs a multimodal interaction strategy to predict target motion more accurately and an improved joint probability data association method to match measurement data with targets. An adaptive root-mean-square cubature Kalman filter is used to estimate the statistical characteristics of noise under complex traffic scenarios with varying process and measurement noise. Experiments conducted on a real vehicle platform demonstrate that the proposed framework improves reliability and robustness in challenging environments. It overcomes the challenges of insufficient data fusion utilization, frequent leakage, and misjudgment of dangerous obstructions around vehicles, and inaccurate prediction of collision risks. The proposed framework has the potential to advance the state of the art in target tracking and perception for intelligent vehicles.

show abstract

“…The system for monitoring is composed of current sensors, an emitter module and two receiver modules to detect electrical discontinuity in rails. Minea et al [ 40 ] present an autonomous/robotic railway vehicle, designed to collect multiple pieces of information on safety and functional parameters of a surface railway and/or subway section, based on data fusion and machine learning.…”

Section: Introductionmentioning

confidence: 99%

Innovative Integrated Solution for Monitoring and Protection of Power Supply System from Railway Infrastructure

Andruşcă

Adam

Dragomir

et al. 2021

Sensors

View full text Add to dashboard Cite

This paper describes an innovative integrated solution for monitoring and protection of the power supply system of electric traction. The development of electronics devices, new possibilities to communicate (wireless), and new sensors makes it possible to design, develop and implement new hardware–software structures in various fields such as energy systems, transportation infrastructure, etc. This contributes to increasing developments in the monitoring and protection of railway infrastructure. A monitoring and protection system that uses sensors and devices to acquire electrical parameters from railway infrastructure has been developed and applied for fault detection and protection of power supply systems from electric traction. The solution of monitoring and protection presented are composed of a hardware–software structure with Global System for Mobile Communications (GSM) communication for monitoring of power supply installations from the electric traction and a central remote system composed of a device with GSM communication and a server that will allow, among others things, accurate detection of the block section (SC), in which an electrical fault (short circuit) has occurred, determination of the circuit breakers electro-erosion from the railway installations and an indication of the opportune moment for maintenance activity, respectively, as well as knowledge of the technical condition of some equipment from the return circuit. The proposed and developed method for monitoring devices has been validated in the railway laboratory to confirm its capability to detect defects and was tested in the field. Experimental results in the field and appropriate data analysis are included in this article.

show abstract

Robotic Railway Multi-Sensing and Profiling Unit Based on Artificial Intelligence and Data Fusion

Cited by 4 publications

References 45 publications

Application and Study of Artificial Intelligence in Railway Signal Interlocking Fault

Application and Study of Artificial Intelligence in Railway Signal Interlocking Fault

Study on Multi-Heterogeneous Sensor Data Fusion Method Based on Millimeter-Wave Radar and Camera

Innovative Integrated Solution for Monitoring and Protection of Power Supply System from Railway Infrastructure

Contact Info

Product

Resources

About