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Multivariate statistical aggregation and dimensionality reduction techniques to improve monitoring and maintenance in railways: The wheelset component

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  • Braga, Joaquim A.P.
  • Andrade, António R.

Abstract

Reliable monitoring and assessment of wear evolutions are critical for performing effective railway maintenance. Several characteristics and variables are used to quantify a worn condition of railway wheelsets. To measure all these wear quantities, emerging inspection technologies are being designed with increasingly complex architectures, working mechanisms and associated high costs. Moreover, data-driven models to support condition-based maintenance to the wheelset easily increase their complexity when too many variables are taken into account and may not provide a straightforward guideline to maintenance decision-makers. The purpose of this paper is to reduce the complexity when describing the wear level, by applying multivariate statistical techniques to real degradation data from railway wheelsets. Several wheelset condition variables and their relationships are analysed. Variables are synthetized through a principal component analysis (PCA) where the varimax rotation effect can be observed. A cluster analysis, which uses the principal components, allows identifying characteristics that lead to different wear evolutions. A strong correlation between the flange thickness and flange slope in the wear process is identified. Differences in wear trajectories between motor and trailer wheelsets are strongly significant. The findings are expected to support the improvement of state monitoring techniques and predictive maintenance optimization models.

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  • Braga, Joaquim A.P. & Andrade, António R., 2021. "Multivariate statistical aggregation and dimensionality reduction techniques to improve monitoring and maintenance in railways: The wheelset component," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:reensy:v:216:y:2021:i:c:s0951832021004488
    DOI: 10.1016/j.ress.2021.107932
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    Cited by:

    1. Liu, Jie & Xu, Yubo & Wang, Lisong, 2022. "Fault information mining with causal network for railway transportation system," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    2. Dai, Xinliang & Qu, Sheng & Sui, Hao & Wu, Pingbo, 2022. "Reliability modelling of wheel wear deterioration using conditional bivariate gamma processes and Bayesian hierarchical models," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    3. He, Rui & Tian, Zhigang & Wang, Yifei & Zuo, Mingjian & Guo, Ziwei, 2023. "Condition-based maintenance optimization for multi-component systems considering prognostic information and degraded working efficiency," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    4. Jin, Yuxue & Geng, Jie & Lv, Chuan & Chi, Ying & Zhao, Tingdi, 2023. "A methodology for equipment condition simulation and maintenance threshold optimization oriented to the influence of multiple events," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    5. Zheng, Niannian & Luan, Xiaoli & Shardt, Yuri A.W. & Liu, Fei, 2024. "Dynamic-controlled principal component analysis for fault detection and automatic recovery," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. Li, Yaxin & Ding, Yuxin & Guo, Yuliang & Cui, Haizhou & Gao, Haiyi & Zhou, Ziyu & (Aaron) Zhang, Nanbo & Zhu, Siyao & Chen, Faan, 2023. "An integrated decision model with reliability to support transport safety system analysis," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    7. Fallahdizcheh, Amirhossein & Wang, Chao, 2022. "Transfer learning of degradation modeling and prognosis based on multivariate functional analysis with heterogeneous sampling rates," Reliability Engineering and System Safety, Elsevier, vol. 223(C).

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