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This subject aims to study the realization of automated continuous assembly of the sealing ring and sealing cover of the holding axle box in the train bogie of the Chinese railway industry. Since the sealing ring and the drive shaft are very precise, the gap is as small as 0.2 mm or less, the outer diameter is 600 mm, the temperature is 150°C, and the weight is 30 kg, so the difficulty of manual assembly is obvious. Aiming at the problems of low efficiency, high labor intensity, and certain safety risks of traditional manual assembly, this paper designs an automated assembly system for robotic hot-assembled sealing rings and sealing covers. The system consists of an operator operation area, a parts preparation area, a shaft parts placement area, and a robotic arm working area, which realizes the automated assembly of the sealing ring and sealing cover. With the innovative design of the gripper at the end of the robotic arm, the system is able to correct errors in the assembly process, thus realizing a precise set of sealing rings. This system improves assembly efficiency, ensures assembly accuracy, and greatly reduces costs. Meanwhile, thermodynamic simulation results show that the system can meet the requirements of assembly temperature under different working conditions.

The axle load acts on the box unit and has shock-pulse nature when the car is moving, that can lead to a mutual change in the surface positions of the roller ends with the edges of the bearing races. The ways are proposed to optimize the internal geometry of the radial and end contact elements of the roller bearing of the axle box unit. It is proposed to eliminate the occurrence of possible conditions that lead to the movement of bearing rollers into the thrust and locking of bearing rollers by increasing the minimum limit in the axle clearance of the axle box.

The centralized power EMU is a new type of passenger transportation equipment for ordinary-speed railways in China, which can greatly improve the efficiency of ordinary-speed railway passenger transportation and improve passenger travel conditions. This paper conducts long-term dynamic tracking experiments on this new type of railway passenger car, studying the evolution laws of wheel wear, axle box vibration, bogie frame vibration, and car body vibration during long-term service, and analyzing typical vehicle dynamics phenomena that occur in long-term tracking experiments. Various studies have shown that the vehicle can maintain good lateral stationarity throughout the entire wheel reprofile cycle, and the growth rate of equivalent conicity gradually slows down as the mileage increases; However, when the mileage after wheel reprofiling is large, harmonic vibration may occur in individual sections of the frame, leading to abnormal vibration of the control car body at 10 Hz. The vertical vibration of the axle box will significantly increase at the rail joint, and its 70 Hz vibration is transmitted to the frame through the experiment suspension, stimulating the bending mode of the crossbeam of the frame, resulting in a more obvious 70 Hz vibration in the vertical direction of the frame. The RMS of the axle box, frame, and body sleeper does not show significant changes with the rise in mileage following wheel reprofiling.

Real-time wheel flat detection by measuring train acceleration can help reduce the risk of more severe damage. Before field implementation, numerical simulations are needed to understand the train dynamic responses under wheel flat conditions. This study employed multibody modelling and numerical simulation to examine how the lateral position of the wheelset and wheel flat length affect the train dynamic responses. Vertical acceleration was measured on the axle boxes. The vertical acceleration fluctuated with the lateral position of the wheelset, being influenced by the wheel flat cross‐section and track lateral irregularities during the simulation run. The wheel flat model affected the amplitude of vertical acceleration from impacts: a newly-formed flat with sharper edges produced higher vertical acceleration than a rounded flat. Longer flat lengths created deeper flats, which caused increased vertical acceleration upon impact. Moreover, higher operating speeds corresponded to more pronounced vertical‐acceleration responses.

The destruction of the bearing ring of the axle box unit inevitably leads to the uncoupling of the car, which causes temporary and material losses. The problem of determining the bearing ring tension without removal is relevant; its solution will improve efficiency and reduce the inspection time. Experimental studies were carried out to determine the forces inside the contact pair, as well as their relationship with the amount of interference. It has been established that with small impacts on the ring, surface deformations in the area of impact are uneven and form a gradient in which large deformations are observed at the edge of the ring, smaller ones in the middle. It has been proven that the transition boundary of the magnitude of deformations depends on the magnitude of the interference in the contact pair. A method for determining the tension by strain gauging is proposed.

Short-wave rail irregularities pose significant risks to high-speed rail safety, as their high-frequency shock features can be effectively captured by axle-box acceleration (ABA) signals. However, visualizing features embedded these signals remains challenging. This paper proposes an improved method for intelligently identifying short-wave rail diseases by combining Adaptive Chirp Mode Decomposition (ACMD), Recurrence Plot (RP), and Convolutional Neural Network (CNN). The method was tested on ABA signals contaminated with rail corrugation and rail impact. In the proposed approach, an ABA signal is first decomposed through ACMD to eliminate noise. Then, the signal is converted to a two-dimensional phase space trajectory image using a recurrence plot. The experimental results demonstrate that RPs of the same rail disease exhibit significant visual similarities, while those of different diseases show distinct patterns. Specifically, RPs of normal signals appear uniform and stochastic with low complexity, RPs of rail corrugation display a periodic checkerboard pattern, and RPs of rail impact feature a prominent cross-shaped void. Finally, a two-dimensional CNN model was developed to adaptively extract irregularity features. The CNN model achieved an accuracy of 96.3%, thus validating its effectiveness in accurately identifying rail impact and rail corrugation, two critical types of short-wave rail irregularities.

Against the background of the continuous increase in the operating speed of the rolling stock, the working environment of the axlebox is becoming more and more severe. To meet the increasing operational demand, the strength and quality requirements of the axle box are also increased accordingly. To realize lightweight design to ensure structural strength, the traditional casting process has the problems of low densities of molded parts, long processing cycles, etc. In contrast, the laser selective melting technology can mold the workpiece with excellent mechanical properties and realize complex structures, effectively solving the above problems. In this paper, the one-piece rotary arm axle box is selected as the research object, its extraordinary load conditions are calculated, and the feasibility of the SLM axle box is verified by hydrostatic simulation. On this basis, the topology optimization design of the axle box is further optimized, and the optimized structural strength and vibration performance are verified by static and modal simulation. SLM technology is used to print components that are ten times smaller in size, and based on the compression experiments, it is verified that its strength meets the design requirements after printing, and the mass of the optimized components is reduced by about 15.4%.

In this paper, an intelligent method to diagnose rail corrugation based on signal decomposition and entropy theory is proposed. The axle box acceleration signals are first decomposed into several components with different frequency bands by ACMP, EEMD and MODWT. By comparison, ACMP is able to successfully extract rail corrugation component from original signal without mode mixing. Energy entropy is then introduced here to quantify the degree of the rate of energy concentration. The analysis results show that the energy will change when rail corrugation occurs and the entropy will become small. It has been also proved that the entropy difference of rail corrugation and normal signal based on ACMP is the most significant. In addition, to intelligently diagnose rail corrugation, we combine energy entropy with energy index and the first mode energy, regarded as the input feature vector of LSSVM, to distinguish rail corrugation from mass data sets. It is obvious that the accuracy of ACMP-based technique is the highest.

The article considers one of the most important wagon units - wheelsets. An analysis was made of the growing number of uncoupled wagons of Russian railways for unplanned repairs and it was revealed that the most common reasons for this are the faulty condition of the wheelsets and axle boxes. It was determined that the factors influencing the nature of the malfunctions are the inappropriate quality of the repair of bogies, the admission to operation of secondary wheels with a rib thickness of less than 26 mm. One of the measures taken is the lubrication of rails and sleepers. The introduction of innovative wagons with wear-resistant wheelsets may also have an impact on reducing the growth in the number of uncoupled wagons requiring repair. The features of newly produced innovative wagons with a higher axle load and an increased turnaround time are considered. To assess the impact of an increase in the share of innovative wagons on the need for wheels, the reliability indicators of wheelsets of innovative gondola and hopper wagons were considered. Reliability was evaluated by indicators of the probability of nonfailure operation, the failure rate parameter and the time between failures. An assessment was carried out for each type of identified malfunctions. The most common were a thin rib and a shelled tread. Results of the calculations showed the dependences of the number of uncoupled wagons on faults from the run of wagons to uncoupling were defined. The probability of nonfailure operation of the wheels of the studied wagons was determined. It has been determined that the average mileage before a malfunction occurs for innovative gondola wagons is 3.5 times greater than for traditional wagons.

Acoustic fault diagnosis technology equipment is non-contact, and the acoustic signal is easy to access. However, it is difficult to extract the feature information of the acoustic signal with low signal-to-noise ratio (SNR). In this paper, a fault diagnosis model (FDM) of axle box bearing based on Chirplet transform (CT) and support vector machine (SVM) is established to diagnose bearing fault based on acoustic signal. The availability of the model is verified by comparing with the vibration acceleration signal bearing fault diagnosis results, and the correctness of the model is verified by utilizing the open database of Western Reserve University. The acoustic-vibration comprehensive bearing fault diagnosis experiment platform (AVEP) is established to investigate the acoustic signal and acceleration signal diagnosis accuracy. The results suggest that, based on the FDM, the diagnosis accuracy and stability of acoustic signal are not as good as acceleration signal when the number of samples is small under the single condition; the diagnosis accuracy of acoustic signal is similar to that of acceleration signal when the number of samples is enough under the multiple condition, which provides a reference for the application of acoustic fault diagnosis technology in engineering in the future.