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Fuzzy Logic-Based Identification of Railway Wheelset Conicity Using Multiple Model Approach

Author

Listed:
  • Erum Saba

    (Institute of Information and Communication Technologies, Mehran University of Engineering and Technology, Jamshoro 76062, Sindh, Pakistan
    Information Technology Centre, Sindh Agriculture University, Tandojam 70060, Sindh, Pakistan)

  • Imtiaz Hussain Kalwar

    (Department of Electrical Engineering, DHA Suffa University, Karachi 75500, Sindh, Pakistan)

  • Mukhtiar Ali Unar

    (Institute of Information and Communication Technologies, Mehran University of Engineering and Technology, Jamshoro 76062, Sindh, Pakistan)

  • Abdul Latif Memon

    (Department of Telecommunication Engineering, Mehran University of Engineering and Technology, Jamshoro 76062, Sindh, Pakistan)

  • Nasrullah Pirzada

    (Department of Telecommunication Engineering, Mehran University of Engineering and Technology, Jamshoro 76062, Sindh, Pakistan)

Abstract

The deterioration of railway wheel tread causes unexpected breakdowns with increasing risk of operational failure leading to higher maintenance costs. The timely detection of wheel faults, such as wheel flats and false flanges, leading to varying conicity levels, helps network operators schedule maintenance before a fault occurs in reality. This study proposes a multiple model-based novel technique for the detection of railway wheelset conicity. The proposed idea is based on an indirect method to identify the actual conicity condition by analyzing the lateral acceleration of the wheelset. It in fact incorporates a combination of multiple Kalman filters, tuned on a particular conicity level, and a fuzzy logic identification system. The difference between the actual conicity and its estimated version from the filters is calculated, which provides the foundation for further processing. After preprocessing the residuals, a fuzzy inference system is used that identifies the actual conicity of the wheelset by assessing the normalized rms values from the residuals of each filter. The proposed idea was validated by simulation studies to endorse its efficacy.

Suggested Citation

  • Erum Saba & Imtiaz Hussain Kalwar & Mukhtiar Ali Unar & Abdul Latif Memon & Nasrullah Pirzada, 2021. "Fuzzy Logic-Based Identification of Railway Wheelset Conicity Using Multiple Model Approach," Sustainability, MDPI, vol. 13(18), pages 1-21, September.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:18:p:10249-:d:635133
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    References listed on IDEAS

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    1. Shaher Momani & Asad Freihat & Mohammed AL-Smadi, 2014. "Analytical Study of Fractional-Order Multiple Chaotic FitzHugh-Nagumo Neurons Model Using Multistep Generalized Differential Transform Method," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-10, June.
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