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.
Highlights
The reneeds to work out practical solutions to produce real time information of the condition of search community still needs to work out practical solutions to produce real time inforthe railway wheelset conicity
This paper proposes the use of lateral acceleration residuals mation of the condition of the railway wheelset conicity
This paper proposes the use of lateral acceleration residuals from the actual wheelset model, and the model that is from the actual wheelset model, and the model that is estimated using Kalman filters, to indirectly identify the conicity value
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Maintaining the conical shape of the wheelset is extremely important for ensuring a smooth wheel–rail contact and proper rolling on the track. The reneeds to work out practical solutions to produce real time information of the condition of search community still needs to work out practical solutions to produce real time inforthe railway wheelset conicity. This paper proposes the use of lateral acceleration residuals mation of the condition of the railway wheelset conicity. This paper proposes the use of lateral acceleration residuals from the actual wheelset model, and the model that is from the actual wheelset model, and the model that is estimated using Kalman filters, to indirectly identify the conicity value. The last section is the conclusion and discusses future work
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