For acoustic roughness monitoring of the railway network at train travelling speed, new direct measurement methods are required. Common direct measurement methods need the blocking of track sections, as they are based on manually operated devices. Indirect measurement methods such as accelerometer or microphone measurements can be installed on the train, but require a conversion of the obtained measurement data to rail roughness. Optical measurement methods allow a direct measurement from the moving train, even at higher speeds, due to the contact-free nature of the measurement. This paper investigates the influence of various disturbances on the measurement result, which are expected on the train. The frequently used chord method deploying laser triangulation sensors is used. Four sensors are integrated into the setup, thus providing the possibility to combine the results from four chord methods. The measurements of the optical system are compared with a tactile measurement of METAS (Swiss Federal Institute of Metrology) on a test bench equipped with a reference rail segment. It is shown that dust and water on the rail have a significant influence in the range of small wavelengths. Displacements and tilting of the sensor array, as well as vibrations, can be compensated to a certain level by the chord method, while a single sensor is significantly disturbed. The combination of four different chord lengths and selection of the theoretically optimal method for each one-third octave band shows an improvement of the measurement result. Based on the observations made, recommendations for practical tests on the train are concluded.

A large part of the noise emissions from rail traffic originates from rolling noise. This is significantly determined by the surface roughness of the wheel and the rail. To quantitatively assess the noise generation from the wheel–rail contact, it is necessary to measure the surface roughness of the rail network. Direct measurements via trolley devices are usually associated with the need for a free track and limitation in velocity. Indirect measurements of rail roughness, such as measuring axle-box accelerations, enable operation during regular passage but only estimate the acoustic roughness. In this study, the potential of an optical and consequently contact-free measurement method using laser triangulation sensors to measure rail roughness from the train is investigated. The approach can combine the advantage of operation during regular passage with the characteristics of a direct measurement, enabling large-scale monitoring of the rail network. A measurement run with a train was carried out on a meter-gauge track at speeds up to . The results of the optical measurement approach were compared with a tactile reference measurement on the track. The results show good agreement of the new measurement setup for dry rail surface conditions at , with a mean deviation of .

The measure for assessing the acoustic quality of the rail surfaces, the acoustic roughness, is defined in the EN 15610 standard. It is shown that this standard contains gaps with regard to the applied procedures for processing the raw data to the quantity of acoustic roughness. Additions to the standard appear necessary to ensure better comparability of the results. A piece of rail tactilely measured by METAS (Swiss Federal Institute of Metrology) was used as a reference. Measurement data recorded by a laser triangulation sensor was used to quantify the adjustments to the standard. This paper provides an overview of the individual processing steps and systematically examines possible additions to the standard to improve the quality of the outcome. Special emphasis was given to a method for outlier removal, pre-filtering, spike removal, curvature correction and calculation of one-third octave bands. It becomes apparent that different implementations can have a significant impact on the final result. The filter used, the wavelength ranges, the methodology for removing outliers should be specified. The spike removal, curvature correction and the calculation of the one-third octave bands should be supplemented in detail to reduce ambiguities in the implementation.

This paper proposes a novel technique to identify rail surface defects using laser triangulation optoNCDT 2300-10LL. Two defect types, squat and flaking, are artificially applied on the surface of a rotary steel ring setup. Various supervised binary classification algorithms are implemented, and their performance in defect identification are compared against each other. Linear classifiers, Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA), are observed to be the most performant. The results also show that in spite of 2-dimensional longitudinal measurement, the collected sensory data can be used effectively to detect defects and potentially be extended to other types along with consideration of multiclassification.