Random-Forest Machine Learning Approach for High-Speed Railway Track Slab Deformation Identification Using Track-Side Vibration Monitoring

Guo, Gaoran; Cui, Xuhao; Du, Bowen

doi:10.3390/app11114756

Cited by 16 publications

(3 citation statements)

References 36 publications

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“…It generates T train sets with less samples than the original sample set. This method has been shown to improve the classification accuracy of the unstable classifiers [37,38]. T train sets are utilized to generate T decision trees.…”

Section: Random Forest Fault Diagnosis Modelmentioning

confidence: 99%

Stator ITSC Fault Diagnosis for EMU Induction Traction Motor Based on Goertzel Algorithm and Random Forest

Wang

et al. 2023

Energies

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The stator winding insulation system is the most critical and weakest part of the EMU’s (electric multiple unit’s) traction motor. The effective diagnosis for stator ITSC (inter-turn short-circuit) faults can prevent a fault from expanding into phase-to-phase or ground short-circuits. The TCU (traction control unit) controls the traction inverter to output SPWM (sine pulse width modulation) excitation voltage when the traction motor is at a standstill. Three ITSC fault diagnostic conditions are based on different IGBTs’ control logics. The Goertzel algorithm is used to calculate the fundamental current amplitude difference Δi and phase angle difference Δθ of equivalent parallel windings under the three diagnostic conditions. The six parameters under the three diagnostic conditions are used as features to establish an ITSC fault diagnostic model based on the random forest. The proposed method was validated using a simulation experimental platform for the ITSC fault diagnosis of EMU traction motors. The experimental results indicate that the current amplitude features Δi and phase angle features Δθ change obviously with an increase in the ITSC fault extent if the ITSC fault occurs at the equivalent parallel windings. The accuracy of the ITSC fault diagnosis model based on the random forest for ITSC fault detection and location, both in train and test samples, is 100%.

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Section: Random Forest Fault Diagnosis Modelmentioning

confidence: 99%

Stator ITSC Fault Diagnosis for EMU Induction Traction Motor Based on Goertzel Algorithm and Random Forest

Wang

et al. 2023

Energies

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“…To verify the feasibility of this measurement method, the results from the DAS, strain gauges, and digital image correlation (DIC) were compared, which showed good consistency. Guo et al developed an intelligent detection method for track slab deformation based on a random forest model and carried out field experiments to verify it [ 56 ]. The test results showed that this intelligent algorithm can effectively identify deformations in track slabs and the recognition rate reaches 96.09%.…”

Section: Applications Of Das In Linear Infrastructure Monitoringmentioning

confidence: 99%

Distributed Acoustic Sensing for Monitoring Linear Infrastructures: Current Status and Trends

Zhu

Liu

Wang

et al. 2022

Sensors

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Linear infrastructures, such as railways, tunnels, and pipelines, play essential roles in economic and social development worldwide. However, under the influence of geohazards, earthquakes, and human activities, linear infrastructures face the potential risk of damage and may not function properly. Current monitoring systems for linear infrastructures are mainly based on non-contact detection (InSAR, UAV, GNSS, etc.) and geotechnical instrumentation (extensometers, inclinometers, tiltmeters, piezometers, etc.) techniques. Regarding monitoring sensitivity, frequency, and coverage, most of these methods have some shortcomings, which make it difficult to perform the accurate, real-time, and comprehensive monitoring of linear infrastructures. Distributed acoustic sensing (DAS) is an emerging sensing technology that has rapidly developed in recent years. Due to its unique advantages in long-distance, high-density, and real-time monitoring, DAS arrays have shown broad application prospects in many fields, such as oil and gas exploration, seismic observation, and subsurface imaging. In the field of linear infrastructure monitoring, DAS has gradually attracted the attention of researchers and practitioners. In this paper, recent research and the development activities of applying DAS to monitor different types of linear infrastructures are critically reviewed. The sensing principles are briefly introduced, as well as the main features. This is followed by a summary of recent case studies and some critical problems associated with the implementation of DAS monitoring systems in the field. Finally, the challenges and future trends of this research area are presented.

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“…Guo et al [37] developed a fiber optic sensing-based approach for monitoring track slab deformation and an adaptive random-forest model-based technique for recognizing track slab displacement. High-speed railways (HSRs) are now being established all around the globe due to their features such as high speed, travel convenience, very low vibration, and noise.…”

Section: Literature Surveymentioning

confidence: 99%

Acoustic Emission-based Damage Detection and Classification in Steel Frame Structure Using Wavelet Transform and Random Forest

Biswas

Datta²,

Topdar³

et al. 2022

Period. Polytech. Civil Eng.

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This research proposes a unique approach for detecting damage locations and identifying damage kinds. This method is beneficial for discovering and categorizing internal structural faults that vision-based approaches cannot locate. Construction-related vibrations in a steel frame structure can be used as a source for acoustic emission. Sensor devices detect the stress waves produced by structure collapse, and spectrum analysis using wavelet transform of such data is valuable in pinpointing the location of the damage. The col-lected characteristics from these signals are input into the most effective RF (Random Forest) classifier, which are used to categories damage types like cracks and bolt loosening. When compared to previous damage localization approaches, the findings show that the proposed strategy is more efficient and has a higher classification accuracy.

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Random-Forest Machine Learning Approach for High-Speed Railway Track Slab Deformation Identification Using Track-Side Vibration Monitoring

Cited by 16 publications

References 36 publications

Stator ITSC Fault Diagnosis for EMU Induction Traction Motor Based on Goertzel Algorithm and Random Forest

Stator ITSC Fault Diagnosis for EMU Induction Traction Motor Based on Goertzel Algorithm and Random Forest

Distributed Acoustic Sensing for Monitoring Linear Infrastructures: Current Status and Trends

Acoustic Emission-based Damage Detection and Classification in Steel Frame Structure Using Wavelet Transform and Random Forest

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