Rails are systematically inspected for internal and surface defects using various non-destructive evaluation (NDE) techniques. During the manufacturing process, rails are inspected using au...

A review on non-destructive evaluation of rails: State-of-the-art and future development:

Introduction to the damage tolerance behaviour of railway rails – a review

Rail inspection based on visual inspection system (VIS) has drawn much attention recently, since VIS is automatic, fast, nondestructive, and objective. However, visual rail inspection is still a challenge to accurately identify possible defects involving a large variety of visual appearances. This paper presents an automatic visual inspection system to address these difficulties. Rail surface images are first obtained by the system, and then fed into the proposed hierarchical inspection framework containing coarse extractor and fine extractor. Specifically, the coarse extractor handles this defect inspection problem by exploring characteristics of rail background instead of defect itself, and focuses on finding the background modes. The fine extractor, which integrates the longitudinal context information and transversal prior information, is presented to largely reduce the impacts of other irregulars. The proposed method is evaluated on labeled Type-I and Type-II rail surface discrete defects data sets and a practical rail line. The experimental results demonstrate that our method outperforms the state-of-the-art works.

A Hierarchical Extractor-Based Visual Rail Surface Inspection System

Rails are among the most important components of railway transportation, and real-time defects detection of the railway is an important and challenging task because of intensity inhomogeneity, low contrast, and noise. This paper presents an automatic railway visual detection system (RVDS) for surface defects and focuses on several key issues of RVDS. First, in view of challenges such as complex condition and orbital reflectance inequality, we put forward a region-of-interest detection region extraction algorithm by vertical projection and gray contrast algorithm. In addition, a curvature filter equipped with implicit computing and surface preserving power is studied to eliminate noise and keep only the details. Then, an improved fast and robust Gaussian mixture model based on Markov random field is established for accurate and rapid surface defect segmentation. Additionally, an expectation–maximization algorithm is applied to optimize the parameters. The experimental results demonstrate that the proposed method performs well with both noisy and railway images, which enables identification and segmentation of the defects from rail surface, achieving detection performance with 92% precision and 88.8% recall rate on average, and is robust compared with the related well-established approaches.

Automatic Visual Detection System of Railway Surface Defects With Curvature Filter and Improved Gaussian Mixture Model

The ability to detect defects in rail, and in particular surface defects such as gauge corner cracking, is essential. Current ultrasonic testing of the rail uses contacting transducers in a send-receive mode which are at present limited in testing speed to around 20-30 mph, and signals from serious surface defects may be masked by nearby shallow surface defects. Using Rayleigh-like surface waves generated and detected in a pitch-catch manner we can overcome the effect of this masking, and by using electro-magnetic acoustic transducers (EMATs) working at a small standoff we may be able to lift many of the current rail testing speed limitations with a high accuracy for detecting and gauging surface defects. We present experiments on rail samples with real and machined defects, using EMATs to generate and detect a low-frequency wide-band surface wave. The depths of the defects can be characterised through analysis of both the time domain signal amplitude and frequency dependent behaviour. The design of EMAT used is discussed, with consideration of signal, frequency content, directionality of the signal and the effect of standoff above the sample.

Characterisation of defects in the railhead using ultrasonic surface waves

For about 150 years, the steel rail has been at the very heart of the world's railway systems. The rail works in a harsh environment and, as part of the track structure, it has little redundancy; thus, its failure may lead to catastrophic derailment of vehicles, the consequences of which can include death, injury, costs and loss of public confidence. These can have devastating and long-lasting effects on the industry. Despite the advances being made in railway permanent way engineering, inspection and rail-making technology, continually increasing service demands have resulted in rail failure continuing to be a substantial economic burden and a threat to the safe operation of virtually every railway in the world. This paper presents an overview of rail defects and their consequences from the earliest days of railways to the present day.

S. L. Grassie

Papers

Rail defects: an overview