A digital-twin-assisted fault diagnosis of railway point machine

Zhang, Shiyao; Dong, Hairong; Maschek, Ulrich; Song, Haifeng

doi:10.1109/dtpi52967.2021.9540118

Cited by 26 publications

(11 citation statements)

References 14 publications

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“…The random forest-based partial discharge pattern recognition model yielded an accuracy of 97% for the test set. A DT-assisted FD framework of railway point machines (RPMs), an essential component in railways that signals the movement direction of the train and simultaneously changes it, is discussed in the work of [ 34 ]. This work aims to identify the possible root causes for malfunctions of the RPM by measuring the current curve of its physical entity and comparing it with the current curve derived from its virtual counterpart.…”

Section: Digital Twin In Fault Monitoringmentioning

confidence: 99%

Exploring Digital Twin-Based Fault Monitoring: Challenges and Opportunities

Bofill,

Abisado,

Villaverde

et al. 2023

Sensors

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High efficiency and safety are critical factors in ensuring the optimal performance and reliability of systems and equipment across various industries. Fault monitoring (FM) techniques play a pivotal role in this regard by continuously monitoring system performance and identifying the presence of faults or abnormalities. However, traditional FM methods face limitations in fully capturing the complex interactions within a system and providing real-time monitoring capabilities. To overcome these challenges, Digital Twin (DT) technology has emerged as a promising solution to enhance existing FM practices. By creating a virtual replica or digital copy of a physical equipment or system, DT offers the potential to revolutionize fault monitoring approaches. This paper aims to explore and discuss the diverse range of predictive methods utilized in DT and their implementations in FM across industries. Furthermore, it will showcase successful implementations of DT in FM across a wide array of industries, including manufacturing, energy, transportation, and healthcare. The utilization of DT in FM enables a comprehensive understanding of system behavior and performance by leveraging real-time data, advanced analytics, and machine learning algorithms. By integrating physical and virtual components, DT facilitates the monitoring and prediction of faults, providing valuable insights into the system’s health and enabling proactive maintenance and decision making.

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Section: Digital Twin In Fault Monitoringmentioning

confidence: 99%

Exploring Digital Twin-Based Fault Monitoring: Challenges and Opportunities

Bofill,

Abisado,

Villaverde

et al. 2023

Sensors

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“…While their work is performed on a bridge, they are concerned about the flatness of the road on top. subject purpose railway power system [61] decrease failure risk, predict destruction of electric equipment railway tracks [62] determine track load, calculate structural stress high-speed train [99] enable new maintenance paradigms railway wheelset, railway pantograph [22] monitor state of railway infrastructure and vehicle, enable prognostic health management railway turnouts [63] estimate stress change railway [64] predictive maintenance railway point machine [65] enable safe train operations light rail transit [66] forecast remaining useful life, prescribe maintenance plans high-speed railway [67] improve infrastructure management, ensure safety railway infrastructure [68] integrate models into holistic platform…”

Section: Diagnostic Applications Of Digital Twinsmentioning

confidence: 99%

“…Estimating stress in tracks [62] and turnouts [63] appear as focuses on their own, as does estimating the position and adhesion of wheels [40]. Researchers are also concerned about decreasing the risk of failure and operating trains safely [61], [65]. Further, digital twins are to serve new maintenance paradigms, such as predictive mainte-nance [64], [99].…”

Section: Diagnostic Applications Of Digital Twinsmentioning

confidence: 99%

A Systematic Mapping Study of Digital Twins for Diagnosis in Transportation

Prikler,

Wotawa

2023

2023 10th International Conference on Dependable Systems and Their Applications (DSA)

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In recent years, digital twins have been proposed and implemented in various fields with potential applications ranging from prototyping to maintenance. Going forward, they are to enable numerous efficient and sustainable technologies, among them autonomous cars. However, despite a large body of research in many fields, academics have yet to agree on what exactly a digital twin is -and as a result, what its capabilities and limitations might be. To further our understanding, we explore the capabilities of digital twins concerning diagnosis in the field of transportation. We conduct a systematic mapping study including digital twins of vehicles and their components, as well as transportation infrastructure. We discovered that few papers on digital twins describe any diagnostic process. Furthermore, most existing approaches appear limited to system monitoring or fault detection. These findings suggest that we need more research for diagnostic reasoning utilizing digital twins.

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“…The applications of digital twins are expanding throughout the industry owing to their advantages such as safety, repeatability, and the low cost of predicting results [ 1 ]. Digital twins are widely used in manufacturing to predict the results of a product, and recently, the scope of autonomous driving has expanded through the digitization of cities [ 2 , 3 , 4 , 5 , 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Optimized Replication of ADC-Based Particle Counting Algorithm with Reconfigurable Multi-Variables in Pseudo-Supervised Digital Twining of Reference Dust Sensor Systems

Lee

Kwon

Park

2023

Sensors

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As the application fields for digital twins have expanded, various studies have been conducted with the objective of optimizing the costs. Among these studies, research on low-power and low-performance embedded devices has been implemented at a low cost by replicating the performance of existing devices. In this study, we attempt to obtain similar particle count results in a single-sensing device replicated from the particle count results in a multi-sensing device without knowledge of the particle count acquisition algorithm of the multi-sensing device. Through filtering, we suppressed the noise and baseline movements of the raw data of the device. In addition, in the process of determining the multi-threshold for obtaining the particle counts, the existing complex particle count determination algorithm was simplified to make it possible to utilize the look-up table. The proposed simplified particle count calculation algorithm reduced the optimal multi-threshold search time by 87% on average and the root mean square error by 58.5% compared to existing method. In addition, it was confirmed that the distribution of particle count from optimal multi-thresholds has a similar shape to that from multi-sensing devices.

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A digital-twin-assisted fault diagnosis of railway point machine

Cited by 26 publications

References 14 publications

Exploring Digital Twin-Based Fault Monitoring: Challenges and Opportunities

Exploring Digital Twin-Based Fault Monitoring: Challenges and Opportunities

A Systematic Mapping Study of Digital Twins for Diagnosis in Transportation

Optimized Replication of ADC-Based Particle Counting Algorithm with Reconfigurable Multi-Variables in Pseudo-Supervised Digital Twining of Reference Dust Sensor Systems

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