Image processing techniques for the detection and characterisation of features and defects in railway tracks

This thesis describes the research that led to the development of a machine vision system in collaboration with TATA, UK and Sheffield Supertram. This was part of a European initiative for Predictive Maintenance employing non-intrusive inspection and data analysis known as PM’n’Idea. The hardware and software design, construction, and evaluation of a prototype for predictive maintenance are presented. The prototype was tested on Sheffield and Warsaw’s tram systems. The prototype has been designed with due account of a specified set of environmental constraints such as a high level of vibrations and space restrictions of the target trams. Special computer vision techniques have been specifically developed to be used with the prototype. Various image processing techniques and algorithms have been evaluated for the purpose of detection and characterisation of a series of rail abnormalities and faults. The system described in this thesis makes use of a number of standard and modified image processing techniques, not only to alleviate the requirements for manual inspections, but also to allow continuous monitoring and tracking of any defects or abnormalities in a rail track. Currently, detecting defects in their earlier stages can only be achieved by using close visual inspection i.e. line walking. Extensive testing and evaluation of the performance of the prototype inspection system at Sheffield Supertram indicated that the system was able to detect abnormalities with a resolution down to 0.1 mm. Evidence of the classification rates for the standard and modified algorithms that are implemented in the system are presented in this thesis. The algorithms developed show an average success rate of 88.9% in detecting surface bound abnormalities

Novel methods of object recognition and fault detection applied to non-destructive testing of rail’s surface during production

A series of rail image inspection algorithms have been developed for Tata Steels Scunthorpe rail production line. The following thesis describes the contributions made by the author in the design and application of these algorithms. A fully automated rail inspection system that has never been implemented before in any such company or setup has been developed. An industrial computer vision system (JLI) already exists for the image acquisition of rails during production at a rail manufacturing plant in Scunthorpe. An automated inspection system using the same JLI vision system has been developed for the detection of rail‟s surface defects during manufacturing process. This is to complement the human factor by developing a fully automated image processing based system to recognize the faults with an improved efficiency and to allow an exhaustive detection on the entire rail in production. A set of bespoke algorithms has been developed from a plethora of available image processing techniques to extract and identify components in an image of rail in order to detect abnormalities. This has been achieved through offline processing of the rail images using the blended use of different object recognition and image processing techniques, in particular, variation of standard image processing techniques. Several edge detection methods as well as adapted well known Artificial Neural Network and Principal Component Analysis techniques for fault detection on rail have been developed. A combination of customised existing image algorithms and newly developed algorithms have been put together to perform the efficient defect detection. The developed system is fast, reliable and efficient for detection of unique artefacts occurring on the rail surface during production followed by fault classification on the rail imaging system. Extensive testing shows that the defect detection techniques developed for automated rail inspection is capable of detecting more than 90% of the defects present in the available data set of rail images, which has more than 100,000 images under investigation. This demonstrates the efficiency and accuracy of the algorithms developed in this work

Development of damage detection algorithms for structural systems based on structural dynamic data

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The development of an autonomous robotic inspection system to detect and characterise rolling contact fatigue cracks in railway track

At present, high speed dual purpose rail/road vehicles employing fixed non-destructive testing (NDT) sensors are used to inspect rails. Due to the uncertainties in characterisation of the defects when they are detected at high speed, manual re-visiting of the defects by expert operators is required before any decision regarding track maintenance is made. This research has been driven by a desire from the rail industry for a robotic system performing faster than human operators and being capable to both detect and characterise rolling contact fatigue (RCF) cracks in rails with the aim of automating the existing manual inspection and enhancing its accuracy and reliability. This thesis combines expert systems technologies with robotic NDT to fulfil this aspiration. A great deal of effort has been spent to develop a robotic inspection trolley which can automatically detect and characterise the RCF cracks in rails using an alternating current field measurement (ACFM) sensor. It uses a rule based expert system (RBES) proposed to control the robotic trolley and more importantly process ACFM data for both detecting and sizing defects. The developed system can detect the possible presence of defects in railway tracks at high speed pass (5-20 km/h) and can automatically return to an identified defect location to perform a slower and more detailed scan (up to 20 mm/s) across a rail section to determine the size, depth and number of cracks present in that section

New techniques for vibration condition monitoring: Volterra kernel and Kolmogorov-Smirnov

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.This research presents a complete review of signal processing techniques used, today, in vibration based industrial condition monitoring and diagnostics. It also introduces two novel techniques to this field, namely: the Kolmogorov-Smirnov test and Volterra series, which have not yet been applied to vibration based condition monitoring. The first technique, the Kolmogorov-Smirnov test, relies on a statistical comparison of the cumulative probability distribution functions (CDF) from two time series. It must be emphasised that this is not a moment technique, and it uses the whole CDF, in the comparison process. The second tool suggested in this research is the Volterra series. This is a non-linear signal processing technique, which can be used to model a time series. The parameters of this model are used for condition monitoring applications. Finally, this work also presents a comprehensive comparative study between these new methods and the existing techniques. This study is based on results from numerical and experimental applications of each technique here discussed. The concluding remarks include suggestions on how the novel techniques proposed here can be improved.Brunel University Department of Mechanical Engineering and CAPES, Fundacao Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior

Advanced Knowledge Application in Practice

The integration and interdependency of the world economy leads towards the creation of a global market that offers more opportunities, but is also more complex and competitive than ever before. Therefore widespread research activity is necessary if one is to remain successful on the market. This book is the result of research and development activities from a number of researchers worldwide, covering concrete fields of research

Optical scattering for security applications

Laser Surface Authentication (LSA) has emerged in recent years as a potentially disruptive tracking and authentication technology. A strong need for such a solution in a variety of industries drove the implementation of the technology faster than the scientific understanding could keep up. The drive to miniaturise and simplify, the need to be robust against real-world problems like damage and misuse, and not least, intellectual curiosity, make it clear that a firmer scientific footing is important as the technology matures. Existing scattering and biometric work are reviewed, and LSA is introduced as a technology. The results of field-work highlight the restrictions which are encountered when the technology is applied. Analysis of the datasets collected in the trial provide, first, an indication of the performance of LSA under real-world conditions and, second, insight into the potential shortcomings of the technique. Using the particulars of the current sensor’s geometry, the LSA signal is characterised. Measurements are made of the decorrelation of the signature with linear and rotational offsets, and it is concluded that while surface microstructure has a strong impact on the rate of decorrelation, this dependency is not driven by the surface’s feature size. A new series of experiments examine that same decorrelation for interference effects with different illumination conditions, and conclude that laser speckle is not an adequate explanation for the phenomenon. The results of this experimental work inform a mathematical description of LSA based on a combination of existing bi-static scattering models used in physics and ray-tracing, which is implemented numerically. The results of the model are found to be a good fit to experimental work, and new predictions are made about LSA