Nonlinear dynamic responses of locomotive excited by rail corrugation and gear time-varying mesh stiffness

Rail corrugation is usually generated in modern railway transportations, such as high-speed railway, urban railway, and heavy-haul railway. It is one of the major excitations to the wheel–rail dynamic interaction, which will cause extra vibration and noise, failures, or even risk of derailment to the vehicle and its components. A dynamics model of a heavy-haul locomotive considering the traction power from the electric motor to the wheelset through gear transmission is employed to investigate the nonlinear dynamic responses of the locomotive. This dynamics model couples the motions of the vehicle, the track, and the gear transmission together. In this dynamics model, excitations from the rail corrugation, the nonlinear wheel–rail contact, the time-varying mesh stiffness, and the nonlinear gear backlash are considered. Then, numerical simulations are performed to reveal the dynamic responses of the locomotive. The calculated results indicate that different nonlinear phenomenon can be observed under the excitation of the rail corrugation with different amplitude and wavelength. The high frequency vibrations excited by the time-varying mesh stiffness are usually modulated by the low frequency vibrations caused by the rail corrugation. However, this is likely to vanish under the chaotic conditions with some corrugation wavelength. The vibration level of the vehicle and the gear transmission increases generally with the corrugation amplitude. However, some corrugation lengths have been found to be more responsible for the vibration of the dynamics system, which should be concerned greatly during the locomotive operation. Meanwhile, involvement of gear transmission systems will cause different dynamic responses between the wheelsets under rail corrugation and gear mesh excitations. First published online 17 January 202

Effect of lateral stiffness of secondary suspensions on heavy-haul locomotives stability during braking based on simulation and experiment

This paper aimed to investigate the effect of the lateral stiffness of secondary suspensions on the stability capacity and running safety of heavy-haul locomotives during braking based on the dynamic model and the field braking tests. The dynamic model of heavy-haul locomotives included two double-unit locomotives and five coupler systems. Simulation results indicate that the increasing of the lateral stiffness of secondary suspensions can improve the stability capacity and running safety of heavy-haul locomotives. Then, the field braking experiments were conducted to validate the dynamic model. Comparing the experiment results of different locomotives, the coupler and carbody yaw angles are respectively decreased by 31.8 and 29.5%, which is consistent with the simulation results. It is worthy to be noted that lateral vibration behaviour of the carbody increases with the increasing of the lateral stiffness of secondary suspensions. For the improved locomotive, the main frequency of lateral acceleration is 1…2 Hz. However, the main frequency of lateral acceleration is 0.5…1 Hz in the original locomotive tests. Moreover, the high-frequency vibration is increased, especially in 10…12.5 Hz. According to the simulation and experiment results, the reasonable lateral stiffness of secondary suspensions is 400 kN/m for the test locomotive

Experimental investigation on dynamic behaviour of heavy-haul railway track induced by heavy axle load

The damage to the track structure and the influence to the line deformation have greatly deteriorated with the increase of the axle load compared with that of the ordinary trains. However, there is a paucity of experimental research on the dynamic influence of the heavier haul freight trains on the railway tracks. The objective of this study is to investigate the dynamic behaviour of heavy-haul railway track induced by heavy axle load by field experimental tests. The wheel–rail dynamic force, the track structure dynamic deformation and the track vibration behaviour are measured and analysed when the train operates in the speed range from 10 to 75 km/h and the axle load of vehicles varies from 21 to 30 t. Comparisons between the results for the axle conditions of 25 and 30 t are made in this paper to reveal the axle load effects. It is demonstrated that part of the indicators reflecting the dynamic behaviour of the railway track increases approximately linearly with the train running speed and axle load, while others are influenced negligibly

Improved Dynamics Model of Locomotive Traction Motor with Elasticity of Rotor Shaft and Supporting Bearings

Abstract The locomotive traction motor is described as a rotor-bearing system coupling the kinetic equations of the traction shaft and its support bearings with the determination of their elastic deformations in this study. Under the effect of excitations induced by the dynamic rotor eccentric distance and time-varying mesh stiffness, the elastic structure deformations of the shaft and support bearings are formulated in the vibration environment of the locomotive. In addition, the nonlinear contact forces between the components of the rolling bearing, the lubricating oil film, and radial clearance are comprehensively considered in this study. The results indicate that the elastic deformations of the shaft and bearings can change the dynamic responses of the traction motor and its support bearings. There are large differences between the ranges of the rotor motion calculated by the rigid and the flexible traction motor models when the intensified wheel-rail interaction is considered. With the increase of the rotor eccentricity, the results underscore the role of the elasticity of traction shaft and support bearings in dynamic researches of the traction motor. The critical value of the initial eccentric distance for the rub-impact phenomenon decreases from 1.23 mm to 1.15 mm considering the flexible effect of the shaft and bearings. This dynamics model of the traction motor can provide more accurate and reasonable simulation results for correlational dynamic researches

Dynamic Influence of Wheel Flat on Fatigue Life of the Traction Motor Bearing in Vibration Environment of a Locomotive

Wheel flat can cause a large impact between the wheel and rail and excites a forced vibration in the locomotive and track structure systems. The working conditions and fatigue life of the motor bearings are significantly affected by the intensified wheel–rail interaction via the transmission path of the gear mesh. In this study, a fatigue life prediction method of the traction motor bearings in a locomotive is proposed. Based on the L−P theory or ISO 281 combined with the Miner linear damage theory and vehicle–track coupled dynamics, the irregular loads induced by the track random irregularity and gear mesh are considered in this proposed method. It can greatly increase the accuracy of predictions compared with the traditional prediction models of a rolling bearing life whose bearing loads are assumed to be constant. The results indicate that the periodic impact forces and larger mesh forces caused by the wheel flat will reduce the fatigue life of the motor bearings, especially when the flat length is larger than 30 mm. Using this method, the effects of the flat length and relatively constant velocity of the locomotive are analyzed. The proposed method can provide a theoretical basis to guarantee safe and reliable working for motor bearings

Fault Prognosis and Diagnosis of an Automotive Rear Axle Gear Using a RBF-BP Neural Network

The rear axle gear is one of the key parts of transmission system for automobiles. Its healthy state directly influences the security and reliability of the automotives. However, non-stationary and nonlinear characteristics of gear vibration due to load and speed fluctuations, makes it difficult to detect and diagnosis the faults from the transmission gear. To solve this problem a fault prognosis and diagnosis method based on a combination of radial basis function(RBF) and back-propagation (BP) neural networks is proposed in this paper. Firstly, a moving average pretreatment is used to suppress the time series fluctuation of vibration characteristic parameter tie series and reduce the interference of random noise. Then, the RBF network is applied to the pretreated parameter sequences for fault prognosis. Furthermore, based on self-learning ability of neural networks, characteristic parameters for different common faults are learned by a BP network. Then the trained BP neural network is utilized for fault diagnosis of the rear axle gear. The results show that the proposed method has a good performance in prognosing and diagnosing different faults from the rear axle gear