Influence of Wheel Profile Wear Coupled with Wheel Diameter Difference on the Dynamic Performance of Subway Vehicles

Abstract

In view of the coexistence of wheel profile wear (WPW) and wheel diameter difference (WDD) on an actual subway line, a dynamic analysis method based on coupling between WPW and equivalent in-phase WDD was proposed. Based on the measurements from a subway vehicle in operation on this line, dynamics modeling and calculations were performed for a single carriage of this vehicle. Later, the interaction between the effects of WPW and equivalent in-phase WDD on the vehicle dynamic performance was analyzed, and the dynamic response in the presence of coupled damage was compared between the outer and inner wheels. Furthermore, the difference in the dynamic response caused by different positions of the larger-diameter wheels (i.e., on the inner track or outer track) was analyzed for the case where equivalent in-phase WDD occurred between the front and rear bogies. The results show that when the vehicle ran on a straight line, the coupling between WPW and WDD reduced the vehicle’s stability but improved its ride comfort. When the vehicle traveled on a curved line, it showed reductions in the lateral wheel/rail contact force, derailment coefficient, axle lateral force, and wear index if the outer wheels had a larger diameter. As a result, the deterioration of the vehicle’s dynamic performance due to the increasing degree of WPW slowed down, and its curve negotiation performance improved. Meanwhile, the outer wheels had significantly greater lateral wheel/rail contact force, derailment coefficient, and wear index compared to the inner wheels. When a −1 mm WDD was coupled with the worn wheel profile for 14 × 104 kilometers traveled, the dynamic performance indexes of the vehicle were close to or even exceeded the corresponding safety limits. The findings can provide technical support for subway vehicle maintenance.

Fault detection and quantitative assessment of wheel diameter difference based on ensemble adaptive tunable Q-factor wavelet transform and mixed kernel principal component analysis

“…Step 2: Implement the adaptive TQWT. The parameter Q is in the range of [1,10] by an increase of 0.1 and r = 3. The maximum decomposition J is determined according to equation (6) for each couple (Q, r).…”

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Rapid measurement method for key dimensions of train wheelset based on improved image processing algorithm

“…In order to ensure the safety of train operation, the key dimensions of train wheelsets must be measured regularly [38][39][40][41]. The working status of the wheelset system must be found in time to provide reliable data for the maintenance department and correctly guide the maintenance of the train wheelset [42,43].…”

Effects of different wheel wear positions on dynamic performance of metro vehicles

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