Abstract Health assessment for point machines is crucial to the safety of rail systems. The operation of the point machine is a typical multi‐stage process, each with its own characteristic features that allow a health assessment. Therefore, to segment various phases self‐adaptively is quite essential to assess the health state of the point machine. Besides, the degradation of the point machine is characterized as non‐linear. However, these issues are barely discussed when assessing the degradation degree. By converting it into a multi‐classification problem, this paper proposes a novel phase segmentation method based on dimensionless time‐domain features and characteristics of time series by utilizing the adaptive Multiclass Mahalanobis Taguchi System (aMMTS) to segment the signal self‐adaptively. Furthermore, this paper proposes a novel algorithm named Non‐linear Dynamic Time Warping (NLDTW), which modifies the conventional Dynamic Time Warping (DTW) by using a non‐linear distance to overcome the lack of global consistency in the non‐linear degradation assessment. Finally, a modified formula of confidence value is presented to assess the actual degradation degree. The efficiency and feasibility of the proposed framework have been verified by the actual data collected from the point machines of Guangzhou Metro.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    A self‐adaptive phase‐segmentation and health assessment framework for point machines


    Beteiligte:
    Ning Wang (Autor:in) / Linlin Kou (Autor:in) / Huiyue Zhang (Autor:in) / Limin Jia (Autor:in) / Yong Qin (Autor:in) / Hongguang Wang (Autor:in) / Zhipeng Wang (Autor:in)


    Erscheinungsdatum :

    2023




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    A self‐adaptive phase‐segmentation and health assessment framework for point machines

    Wang, Ning / Kou, Linlin / Zhang, Huiyue et al. | Wiley | 2023

    Freier Zugriff

    Self-test framework for self-adaptive software architecture

    Roopa, Y. Mohana / Babu, M. Ramesh | IEEE | 2017


    Foreground Segmentation Using Adaptive 3 Phase Background Model

    Roy, Sujoy Madhab / Ghosh, Ashish | IEEE | 2020


    Self-adaptive segmentation for infrared satellite cloud image [4550-76]

    Wang, P. / Xue, J. / Liu, Z. et al. | British Library Conference Proceedings | 2001


    Self-folding machines

    FELTON SAMUEL M / WOOD ROBERT J / TOLLEY MICHAEL T et al. | Europäisches Patentamt | 2018

    Freier Zugriff