Detection Of Rail Surface Defects Research Articles

Adaptive noise cancelling and time–frequency techniques for rail surface defect detection

Adaptive noise cancelling (ANC) is a technique which is very effective to remove additive noises from the contaminated signals. It has been widely used in the fields of telecommunication, radar and sonar signal processing. However it was seldom used for the surveillance and diagnosis of mechanical systems before late of 1990s. As a promising technique it has gradually been exploited for the purpose of condition monitoring and fault diagnosis. Time–frequency analysis is another useful tool for condition monitoring and fault diagnosis purpose as time–frequency analysis can keep both time and frequency information simultaneously. This paper presents an ANC and time–frequency application for railway wheel flat and rail surface defect detection. The experimental results from a scaled roller test rig show that this approach can significantly reduce unwanted interferences and extract the weak signals from strong background noises. The combination of ANC and time–frequency analysis may provide us one of useful tools for condition monitoring and fault diagnosis of railway vehicles.

Railway wheel-flat and rail surface defect modelling and analysis by time–frequency techniques

Damage to the surface of railway wheels and rails commonly occurs in most railways. If not detected, it can result in the rapid deterioration and possible failure of rolling stock and infrastructure components causing higher maintenance costs. This paper presents an investigation into the modelling and simulation of wheel-flat and rail surface defects. A simplified mathematical model was developed and a series of experiments were carried out on a roller rig. The time–frequency analysis is a useful tool for identifying the content of a signal in the frequency domain without losing information about its time domain characteristics. Because of this, it is widely used for dynamic system analysis and condition monitoring and has been used in this paper for the detection of wheel flats and rail surface defects. Three commonly used time–frequency analysis techniques: Short-Time Fourier Transform, Wigner–Ville transform and wavelet transform were investigated in this work.

Railway Wheel Flat and Rail Surface Defect Detection by Time-frequency Analysis

Damage to the surface of railway wheels and rails commonly occurs in most railways and, if not detected at an early stage, can result in rapid deterioration and possible failure incurring high maintenance costs. If detected at an early stage these maintenance costs can be minimised. This paper presents an investigation into the use of time-frequency analysis of vibrations in railway vehicles for early detection of wheel flats and rail surface defects. Time-frequency analysis is a useful tool for identifying the content of a signal in the frequency domain without losing information about its time domain characteristics. Three commonly used time-frequency analysis techniques: Short-Time-Fourier-Transform (STFT); Wigner-Ville Transform (WVT); and Wavelet Transform (WT) are investigated in this work.

Detection and evaluation of rail surface defects using alternating current field measurement techniques

Reliable and cost‐effective inspection of rail tracks is of paramount importance to ensure the safety of rolling stock operations. In this paper alternating current field measurement (ACFM) sensors are used to carry out experiments on artificially induced rail surface defects at various speeds using testing configurations that simulate actual inspection conditions found in the field. From the obtained results it can be clearly seen that the ACFM sensors can detect the artificially induced rail surface defects even when relatively significant lift-off is involved, i.e. ∼5 mm and that the effect of increasing speed on the amplitude of the Bx signal, which is directly related to the depth of the crack, is negligible. However, clustered defects cannot be easily resolved and the overall amplitude is related to the spacing of the defects within a cluster. The order of clustered defects also significantly influences the maximum amplitude of the recorded Bx signal. The validity of the results obtained from the tests on artificially induced defects was verified by conducting further ACFM tests on a rail sample removed from service that contained mild rolling contact fatigue cracks.

An Algorithm for the Detection and Measurement of Rail Surface Defects

Abstract Defects on the surface of railroad tracks have been the cause of growing concern over the past three decades. The automated detection and classification of rail surface defects would be of great assistance to rail maintenance planners, who develop grinding strategies to prevent the development of potentially dangerous deterioration. Videotaped images of the surface of rail have been obtained, but they are subject to distortions due to the acquisition process as well as physical phenomena on the track itself. In this analysis, an algorithm is presented for the simultaneous restoration and segmentation of objects in a two-dimensional image. The algorithm relies on distributions that model the relationships between sites and neighbors in order to restore a distorted image to an estimate of its ideal form, and also obtain detailed information about the objects located in the image. The foundation of the algorithm is the Iterated Conditional Modes procedure for image restoration. The resulting extensi...

An Algorithm for the Detection and Measurement of Rail Surface Defects

An Algorithm for the Detection and Measurement of Rail Surface Defects