Bearing fault diagnosis of a rotating machine plays an important role in reliable operation. A novel intelligent fault diagnosis method for roller bearings has been developed based on a proposed hybrid classifier ensemble approach and the improved Dempster-Shafer theory. The improved Dempster-Shafer theory well considered the combination of unreliable evidence sources, the uncertainty information of basic probability assignment, and the relative credibility of the evidence on the weights in the process of decision making under the framework of fuzzy preference relations, which can effectively deal with conflicts of the evidences and then well improve the diagnostic accuracy for the hybrid classifier ensemble. The effectiveness of the improved Dempster-Shafer theory has been verified via a numerical example. In addition, deep neural networks, a support vector machine, and extreme learning machine techniques have been utilized in the single-stage classification based on singular spectrum entropy, power spectrum entropy, time-frequency entropy, and wavelet packet energy spectrum entropy in this work. Performances of the proposed hybrid ensemble classifier has been demonstrated on a bearing test-rig, compared with the original Dempster-Shafer theory. It can be found that the overall error rate can be greatly reduced with the hybrid ensemble classifier and the improved Dempster-Shafer theory.

Purpose: The purpose of this paper is to study the wear evolution of metro wheels under the conditions of different track sequences, track composition and vehicle load and then to predict wheel wear and to guide its maintenance.Methodology: By using the SIMPACK and MATLAB software, numerical simulation analysis of metro wheel wear is carried out based on Hertz theory, the FASTSIM algorithm and the Archard model. First of all, the vehicle dynamics model is established to calculate the motion relationship and external forces of wheel-rail in the SIMPACK software. Then, the normal force of wheel-rail is solved based on Hertz theory, and the tangential force of wheel-rail is calculated based on the FASTSIM algorithm through the MATLAB software. Next, in the MATLAB software, the wheel wear is calculated based on the Archard model, and a new wheel profile is obtained. Finally, the new wheel profile is re-input into the vehicle system dynamics model in the SIMPACK software to carry out cyclic calculation of wear.Findings: The results show that the setting order of different curves has an obvious influence on wear when the proportion of the straight track and the curve is fixed. With the increase in running mileage, the severe wear zone is shifted from tread to flange root under the condition of the sequence-type track, but the wheel wear distribution is basically stable for the unit-type track, and their wear growth rates become closer. In the tracks with different straight-curved ratio, the more proportion the curved tracks occupy, the closer the severe wear zone is shifted to flange root. At the same time, an increase in weight of the vehicle load will aggravate the wheel wear, but it will not change the distribution of wheel wear. Compared with the measured data of one city B type metro in China, the numerical simulation results of wheel wear are nearly the same with the measured data.Practical implications: These results will be helpful for metro tracks planning and can predict the trend of wheel wear, which has significant importance for the vehicle to do the repair operation. At the same time, the security risks of the vehicle are decreased economically and effectively.Originality/value: At present, many scholars have studied the influence of metro tracks on wheel wear, but mainly focused on a straight line or a certain radius curve and neglected the influence of track sequence and track composition. This study is the first to examine the influence of track sequence on metro wheel wear by comparing the sequence-type track and unit-type track. The results show that the track sequence has a great influence on the wear distribution. At the same time, the influence of track composition on wheel wear is studied by comparing different straight-curve ratio tracks; therefore, wheel wear can be predicted integrally under different track conditions.

A new phenomenological model, the TP (Temperature Phase) model, is presented to carry out optimization calculations for turbulent diffusion combustion in a high-pressure common rail diesel engine. Temperature is the most important parameter in the TP model, which includes two parts: an auto-ignition and a soot model. In the auto-ignition phase, different reaction mechanisms are built for different zones. For the soot model, different methods are used for different temperatures. The TP model is then implemented in KIVA code instead of original model to carry out optimization. The results of cylinder pressures, the corresponding heat release rates, and soot with variation of injection time, variation of rail pressure and variation of speed among TP model, KIVA standard model and experimental data are analyzed. The results indicate that the TP model can carry out optimization and CFD (computational fluid dynamics) and can be a useful tool to study turbulent diffusion combustion.

Considering the aerodynamic loads arising during the process of two high-speed trains passing each other (hereinafter referred to as TPEO) as well as different track excitations on different railway lines, this study examined the combined effect of TPEO and different track excitations on wheel wear. A vehicle system dynamics model was built using the actual parameters of a high-speed train. The wheel/rail contact problems were analyzed using a combination of semi-Hertzian contact theory and Kalker’s simplified theory. Elkins wear index was used as a criteria for evaluation of wheel wear. The additional excitation arising from the pressure wave during TPEO at a constant speed of 300 km/h was applied to the model. Then the effect of the aerodynamic excitation due to TPEO on wheel wear under four track excitation conditions was investigated for the whole train, different cars, and different axles. The results show that under different track excitations, the train’s wheel wear increased in the case where one TPEO event occurred compared to that in the case of no TPEO event. Under the four track conditions (no track excitation, the track excitations on the Wuhan–Guangzhou high-speed railway, German track excitation spectra, and track excitation on the Beijing–Tianjin intercity railway), the total wheel wear increased by 17.36%, 0.69%, 0.82%, and 1% respectively over the entire trip, and increased by 1600%, 65.77%, 78.62%, and 79.73% at the moment of TPEO. A comparison between different cars shows that under different track excitation conditions, the tail car’s wheel wear was the most affected by TPEO, while the head car’s wheel wear was the least affected. A similar pattern is observed in the variation in transverse displacement of wheels under TPEO between different cars. For each bogie, TPEO under different track excitations had a greater effect on the front-axle wheel wear than on the rear-axle wheel wear.

This paper aims to investigate the influence of different speed at the end of the constant power section of the traction characteristic curve (TCC) on the vehicle dynamic performance and evaluate the TCC. A dynamics co-simulation model is developed in SIMPACK and MATLAB for dynamic analysis of the urban rail vehicle (URV) under different traction characteristics. Then it is used to investigate how acceleration distance and vehicle dynamic performance varies with the speed at the end of the constant power section of the TCC under different starting torques. Moreover, the entropy weight TOPSIS method which is widely used to solve the multi-objective decision-making problem is chosen to evaluate the traction characteristics based on various dynamic performance indexes over 25 test sections and acceleration distance. The results show that under traction conditions, the starting torque has effects on the lateral and vertical accelerations of car body, lateral and vertical wheel-rail contact forces, derailment coefficient and wheel unloading factor. The higher the torque, the greater the value of each index, that means the poorer the vehicle ride characteristics and running safety. For a given starting torque, the dynamic performance indexes increase with the increase of the speed at the end of the constant power section from 50 km/h to 80 km/h during the vehicle speed-up process. The maximum growth rate of the lateral and vertical accelerations of car body, lateral and vertical wheel-rail contact forces are 17%, 8%, 8% and 2%, respectively, when the speed at the end of the constant power section increases from 50 km/h to 80 km/h. Last, the comprehensive evaluation of traction characteristics using entropy-weight TOPSIS method reveals when the starting torque is 800 N · m , 1000 N · m , 1200 N · m and 1400 N · m , the corresponding optimal speed at the end of the constant power section is 80 km/h, 80 km/h, 70 km/h and 50 km/h, respectively. The result of the study can provide theoretical support for traction characteristic design and traction control for URVs.