Advanced remote condition monitoring of railway infrastructure and rolling stock

The rail network is an integral part of the modern transport system. Railway transportation accommodates the mobility of both passengers and goods cost-effectively and in an environmentally friendly way at large scale. Thus, the contribution of railway transportation to the global economy, sustainable growth and mitigation of climate change effects is profound. Rail operations have become more intense, with traffic density continuously increasing. At the same time rolling stock speed and axle loads have also increased. This has led to strong interest in re-evaluating the fundamentals of the way rail infrastructure and rolling stock are currently inspected and maintained. Recent attention has focused on the development of advanced remote condition monitoring techniques for the assessment of the structural integrity of critical rail infrastructure and rolling stock components. The widespread implementation of effective and proven remote condition monitoring technologies can result in the meaningful reduction of the demand for conventional time-consuming and costly inspection methodologies, helping increase the availability and hence capacity factor of the rail network. This paper presents the most recent developments and results obtained from the experimental work carried out by the authors on remote condition monitoring of rail infrastructure and rolling stock components using acoustic emission and vibration analysis techniques under laboratory and field conditions

Multivariable Analysis for Advanced Analytics of Wind Turbine Management

Operation and maintenance tasks on the wind turbines have an essen- tial role to ensure the correct condition of the system and to minimize losses and increase the productivity. The condition monitoring systems installed on the main components of the wind turbines provide information about the tasks that should be carried out over the time. A novel statistical methodology for multivariable analysis of big data from wind turbines is presented in this paper. The objective is to analyse the necessary information from the condition monitoring systems installed in wind farms. The novel approach filters the main parameters from the collected signals and uses advanced computational techniques for evaluating the data and giving mean- ing to them. The main advantage of the approach is the possibility of the big data analysis based on the main information available

Measurement of phase transformations in steel using electromagnetic sensors.

An electromagnetic (EM) sensor, capable of detecting the formation of ferromagnetic ferrite from paramagnetic austenite below the Curie temperature, has been developed and assessed. The long term aim of this work is to develop a method for monitoring microstructure online during strip steel processing. In the present paper, the initial results of variation in trans-impedance with microstructure obtained for three different types of steel with varying carbon contents are discussed. It was found that the EM sensor can successfully detect the formation of ferrite below the Curie temperature, but trans-impedance values are affected by the presence of a decarburised ferrite ring that forms around the specimens tested in a furnace. It was also found that the trans-impedance value is monotonically (non-linearly) related to ferrite volume fraction, and depends on the morphology and distribution of the ferromagnetic phase and, hence, is influenced by the prior austenite grain size

Detection and measurement of phase transformation in steels using electromagnetic sensors – experimental results and modelling simulations.

An electromagnetic (EM) sensor, capable of detecting the formation of ferromagnetic ferrite from paramagnetic austenite below the Curie temperature, has been developed and assessed. In this article, results obtained using an a.c. EM sensor for a medium (0.45 wt pct)—carbon steel slow cooled through its transformation-temperature range are presented. It was found that the EM sensor can successfully detect the formation of ferrite below the Curie temperature, but that the transimpedance values can be significantly affected by the formation of a decarburized ferrite ring around the samples. It was also found that the transimpedance value is monotonically (nonlinearly) related to the ferrite volume fraction and depends on the morphology/distribution of the ferromagnetic phase and, hence, is influenced by the prior-austenite grain size. Results from finite-element (FE) simulations designed to enable prediction of the transimpedance from the microstructure are also presented, showing that two-dimensional (2-D) FE simulations can be successfully used to model the experimental trends observed. The combination of modeling and measurement has shown that EM sensors can be used to indirectly monitor the ferrite transformation (below the Curie temperature), thus providing a measure of ferrite volume fraction and also a means of identifying the ferrite distribution in the microstructure

Effect of microstructural variations on smart inductive sensor measurements of phase transformation in steel.

This paper reports the effect of microstructural variations, particularly decarburisation, on the signal from an electromagnetic sensor used to detect ferrite formation from austenite below the Curie temperature. The decarburisation type (full or partial) and sensor frequency affect the amount the signal is dominated by the surface decarburised region