Prognosis of wheel tread degradation status based on PCA-NARX neural network

Huang, B. Q.; Mingcheng, E; Jiang, Zhenan; Li, Qi

doi:10.1109/phm-shanghai49105.2020.9280951

Cited by 2 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experimental results demonstrated that both approaches achieve good performance in joint detection. A nonlinear autoregressive neural network with principal component analysis information fusion was used in the study [ 91 ] to obtain the relationship between wheel tread wear and external related variables. The ways for further improvement of prognosis have been analyzed.…”

Section: Statistical Study Of Relation Between Sleeper Support Conditionsmentioning

confidence: 99%

Identification of Sleeper Support Conditions Using Mechanical Model Supported Data-Driven Approach

Sysyn

Przybyłowicz

Nabochenko

et al. 2021

Sensors

View full text Add to dashboard Cite

The ballasted track superstructure is characterized by a relative quick deterioration of track geometry due to ballast settlements and the accumulation of sleeper voids. The track zones with the sleeper voids differ from the geometrical irregularities with increased dynamic loading, high vibration, and unfavorable ballast-bed and sleeper contact conditions. This causes the accelerated growth of the inhomogeneous settlements, resulting in maintenance-expensive local instabilities that influence transportation reliability and availability. The recent identification and evaluation of the sleeper support conditions using track-side and on-board monitoring methods can help planning prevention activities to avoid or delay the development of local instabilities such as ballast breakdown, white spots, subgrade defects, etc. The paper presents theoretical and experimental studies that are directed at the development of the methods for sleeper support identification. The distinctive features of the dynamic behavior in the void zone compared to the equivalent geometrical irregularity are identified by numeric simulation using a three-beam dynamic model, taking into account superstructure and rolling stock dynamic interaction. The spectral features in time domain in scalograms and scattergrams are analyzed. Additionally, the theoretical research enabled to determine the similarities and differences of the dynamic interaction from the viewpoint of track-side and on-board measurements. The method of experimental investigation is presented by multipoint track-side measurements of rail-dynamic displacements using high-speed video records and digital imaging correlation (DIC) methods. The method is used to collect the statistical information from different-extent voided zones and the corresponding reference zones without voids. The applied machine learning methods enable the exact recent void identification using the wavelet scattering feature extraction from track-side measurements. A case study of the method application for an on-board measurement shows the moderate results of the recent void identification as well as the potential ways of its improvement.

show abstract

Section: Statistical Study Of Relation Between Sleeper Support Conditionsmentioning

confidence: 99%