The transient impulse features caused by rolling bearing faults are often present in the resonance frequency band which is closely related to the dynamic characteristics of the machine structure. Informative frequency band identification is a crucial prerequisite for envelope analysis and thereby accurate fault diagnosis of rolling bearings. In this paper, based on the ratio of quasi-arithmetic means and Gini index, improved Gini indices (IGIs) are proposed to quantify the transient impulse features of a signal, and their effectiveness and advantages in sparse quantification are confirmed by simulation analysis and comparisons with traditional sparsity measures. Furthermore, an IGI-based envelope analysis method named IGIgram is developed for fault diagnosis of rolling bearings. In the new method, an IGI-based indicator is constructed to evaluate the impulsiveness and cyclostationarity of the narrow-band filtered signal simultaneously, and then a frequency band with abundant fault information is adaptively determined for extracting bearing fault features. The performance of the IGIgram method is verified on the simulation signal and railway bearing experimental signals and compared with typical sparsity measures-based envelope analysis methods and log-cycligram. The results demonstrate that the proposed IGIs are efficient in quantifying bearing fault-induced transient features and the IGIgram method with appropriate power exponent can effectively achieve the diagnostics of different axle-box bearing faults.

This paper investigates the wear rate and pattern for wheels turned with thin flanges using Economic Tyre turning (ETT). ETT refers to the process of turning wheels to a profile that has the same tread shape but a thinner flange than the design case profile, allowing less material to be removed from the wheel diameter during re-profiling. Modern wheel lathes are typically capable of turning such profiles but GB railway group standards do not currently permit their use.The paper demonstrates how the Wheel Profile Damage Model (WPDM) [1] can be used, with a good degree of accuracy, to predict both the magnitude of wheel wear and the worn profile shape of the design and ETT re-profiled wheels for service mileages exceeding 100,000 miles. The WPDM simulations were run for two typical Electric Multiple Units (EMU) (one suburban and one inter-city train fleet) and a 2-axle freight wagon.Additionally, it discusses the calibration methodology used to adjust the wear coefficients contained within the Archard wear model to improve the accuracy of the WPDM simulation results for specific routes and vehicle types. Furthermore, this paper presents the findings of a trial of ETT on a fleet of inter-city trains.The analysis is extended to predict the effect of using ETT on rail RCF for typical routes and operating conditions using a series of vehicle dynamic simulations. The analysis considers new 56E1 and 60E2 rails together with a selection of worn wheel.The research provides valuable evidence to support a future change to the standards which will allow train operators/maintainers to implement ETT policies.

No abstract

No abstract

No abstract