Academic literature on the topic 'Axlebox bearings'

Defects in railway axle bearings can affect operational efficiency, or cause in-service failures, damaging the track and train. Healthy bearings produce a certain level of vibration and noise, but a bearing with a defect causes substantial changes in the vibration and noise levels. It is possible to detect the bearing defects at an early stage of their development, allowing an operator to repair the damage before it becomes serious. When a vehicle is scheduled for maintenance, or due for overhaul, knowledge of bearing damage and severity is beneficial, resulting in fewer operational problems and optimised fleet availability. This paper is a review of the state of the art in condition monitoring systems for rolling element bearings, especially the axlebox bearings. This includes exploring the sensing technologies, summarising the main signal processing methods and condition monitoring techniques, i.e. wayside and on-board. Examples of commercially available systems and outputs of current research work are presented. The effectiveness of the current monitoring technologies is assessed and the p– f curve is presented. It is concluded that the research and practical tests on axlebox bearing monitoring are limited compared to the generic bearing applications.

A real time and effective axlebox bearing fault diagnostic method is significant in the condition-based maintenance. In the axlebox bearing fault diagnostic system, fault features extraction and fault patterns classification are two important aspects to identify whether a axlebox bearing is failure or not. This paper presents a method of axlebox bearing fault diagnosis based on wavelet packet decomposition and BP neural network. First decompose the vibration signal into a finite number of coefficients by wavelet packet decomposition. Then calculate energy moment of each coefficient and take the energy moment as an eigenvector to effectively express the failure feature. Finally BP neural network is used for fault classification. The experimental results show that combining wavelet packet decomposition with BP neural network could identify the axlebox bearing fault effectively. The average diagnosis accuracy rate is 96.67%.

Increasing service time makes the axlebox bearing of railway vehicle vulnerable to develop a fault in inner or outer races, which can cause some serious adverse effects on a railway vehicle’s safe operation. To tackle this problem, we established a railway vehicle vertical-longitudinal dynamic model with inner/outer races faults of axlebox bearing and validated it by experimental data. We utilized the time-synchronous average (TSA) technology to filter the raw signals and studied their vibration features. The results show that the longitudinal vibration features are more sensitive for inner race fault identification, while the vertical vibration features are more suitable for outer race fault identification. For inner race fault identification, the indicator peak-to-peak value (PPV) that increases 1056% relative to the healthy state at the most severe fault performs the best sensitivity. For outer race fault identification, the indicator skewness value (SV) that increases 518% relative to the healthy state at the most severe fault exhibits the best performance. The research work can provide meaningful guidance for accurate diagnosis of axlebox bearing faults of railway vehicles.

This article describes the actual problem for railway transport, maximum thrust without boxing. The classification for dynamic modes operation of the locomotive drive is presented. An example of a system built for locomotive is presented. It is possible to use an encoder and vibration sensor inside a smart axle-box. These sensors based on a specially domestic designed ASIC. It is shown that vibration sensors could be positioned on the cover of an axle-box. Considering this fact, one could develop a system for predicting “before boxing” state of a wheel pair. That system could be based on domestically produced electronic components. This article is about developing a smart device for railway transport, in particular, for traction vehicle. The operating principle is based on express analysis of dynamic processes in the contact of the wheel with the rail, the characteristics of which are determined by the angular accelerations of the wheel pair and forward accelerations of the axle box, i.e. the bearing housing, which is rotating in the wheelset axle.

Axlebox bearings are one of the most critical components of a rail vehicle with regard to safety. An axlebox bearing that breaks during operation can be dangerous for the passengers and expensive for the operator. In-service failure of axlebox bearings has been the cause of many catastrophic accidents. Thus, it is of utmost importance to predict bearing failures as early as possible. This will increase reliability and safety of the vehicle as well as reduce the vehicle maintenance cost. Monitoring of roller bearings is an active research eld, and many methods have been proposed by other researchers. Many of these methods employ complex algorithms to make the most use of the given measurements. The algorithms often lack interpretability and have high computational costs, making them dicult to employ in an on-board system. This thesis proposes an interpretable and transparent algorithm that predicts bearing damages with high accuracy. Meanwhile, it tries to retain interpretability as much as possible. The algorithm is based on Empirical Mode Decomposition (EMD) and Singular Value Decomposition (SVD). These two techniques extract essential and meaningful information from the axlebox accelerations. The algorithm is benchmarked on two benchmark datasets, and the results are compared to the respective literature. Then the algorithm is employed on the railway axlebox acceleration measurements that were taken on an axlebox test bench available at SWJTU. The proposed algorithm can be extended to incorporate additional measurements of dierent types, e.g. sound or temperature measurements. The incorporation of other types of measurements will improve the performance of the algorithm even further.
Axelbox lager är en av de viktigaste komponenterna i ett järnvägsfordon när det berör säkerheten. Ett axelbox lager som havererar under drift kan vara farligt for passagerarna och även dyrt för operatören. Driftfel av lagren har varit orsaken till många katastrofala olyckor. Därför är det av yttersta vikt att förutsäga lagerfel så tidigt som möjligt. Detta ökar fordonets tillförlitlighet och säkerhet samt minskar underhållskostnaderna. Mycket forskning har utförts inom övervakning av rullager. Många metoder använder komplexa algoritmer för att maximalt utnyttja matningarna. Algoritmerna saknar ofta tolkbarhet och har höga beräkningskostnader, vilket gör dem svåra att använda i ett integrerat system. Denna avhandling kombinerar era metoder för databehandling och maskininlärning till en algoritm som kan förutsäga lagerskador med hög precision, samtidigt som tolkningsförmågan bibehalls. Bland andra välkända metoder sa använder algoritmen Empirical Mode Decomposition (EMD) och Singular Value Decomposition (SVD) för att extrahera väsentlig information for vibrationsmätningarna. Algoritmen testas sedan med tre olika vibrationsdatamängder, varav en mättes specikt med tanke på simulering av axelbox lager. Ett annat mål med algoritmen är att göra den tillämpad för ytterligare mätningar. Det bör vara möjligt att inkludera mätningar av olika slag, dvs ljud- eller temperaturmätningar, och därigenom förbättra resultaten. Detta skulle minska implementeringskostnaden avsevärt eftersom befintliga sensorer används för detta ändamål. I händelsen av att de föreslagna metoderna inte fungerar med nya mätningar är det även möjligt att integrera ytterligare funktioner i algoritmen.