Experimental Validation of Multi-Sensor Data Fusion Model for Railway Wheel Defect Identification



Published Jul 2, 2018
Alireza Alemi Yusong Pang Gabriel Lodewijks


Wheel defects are detrimental for railway train and track components and should be detected and identified as early as possible. Wheel Impact Load Detector (WILD) is a commercial condition monitoring system used for detecting the defective wheels. This system usually measures the rail strain at different points by multiple sensors. WILD converts the measured strains to the force and uses the peak force, dynamic force, and ratio of the peak force to the static force to estimate the condition of the in-service wheels. These methods are useful for detecting the severe defects contributing to the contact force to the extent that exceed a predetermined threshold. Therefore, in the prior research a fusion method has been developed to reconstruct a new informative pattern from the data collected by the multiple sensors. The reconstructed pattern provides a comprehensive description of the wheel condition. This paper validates the fusion method using a set of lab tests to investigate the applicability of the proposed method. For this purpose, a test rig has been built consisting of a circular rail, a rotating arm, and a wheel. Six strain sensors have been installed under the rail in the symmetric locations over the rail circle with 60 degree intervals. The fusion method used to reconstruct a signal from the bending strain signals measured by the multiple sensors. Different wheel defects including the flat and out-of-round wheels have been tested and the results validated the fusion method by providing informative patterns.

How to Cite

Alemi, A., Pang, Y., & Lodewijks, G. (2018). Experimental Validation of Multi-Sensor Data Fusion Model for Railway Wheel Defect Identification. PHM Society European Conference, 4(1). https://doi.org/10.36001/phme.2018.v4i1.364
Abstract 390 | PDF Downloads 334



railway, wheel-rail contact, condition monitoring, diagnosis, defect detection

Technical Papers