Since the last decade the problem of surface inspection has been receiving great attention from the scientific community, the quality control and the maintenance of products are key points in several industrial applications.The railway associations spent much money to check the railway infrastructure. The railway infrastructure is a particular field in which the periodical surface inspection can help the operator to prevent critical situations. The maintenance and monitoring of this infrastructure is an important aspect for railway association.That is why the surface inspection of railway also makes importance to the railroad authority to investigate track components, identify problems and finding out the way that how to solve these problems. In railway industry, usually the problems find in railway sleepers, overhead, fastener, rail head, switching and crossing and in ballast section as well. In this thesis work, I have reviewed some research papers based on AI techniques together with NDT techniques which are able to collect data from the test object without making any damage. The research works which I have reviewed and demonstrated that by adopting the AI based system, it is almost possible to solve all the problems and this system is very much reliable and efficient for diagnose problems of this transportation domain. I have reviewed solutions provided by different companies based on AI techniques, their products and reviewed some white papers provided by some of those companies. AI based techniques likemachine vision, stereo vision, laser based techniques and neural network are used in most cases to solve the problems which are performed by the railway engineers.The problems in railway handled by the AI based techniques performed by NDT approach which is a very broad, interdisciplinary field that plays a critical role in assuring that structural components and systems perform their function in a reliable and cost effective fashion. The NDT approach ensures the uniformity, quality and serviceability of materials without causing any damage of that materials is being tested. This testing methods use some way to test product like, Visual and Optical testing, Radiography, Magnetic particle testing, Ultrasonic testing, Penetrate testing, electro mechanic testing and acoustic emission testing etc. The inspection procedure has done periodically because of better maintenance. This inspection procedure done by the railway engineers manually with the aid of AI based techniques.The main idea of thesis work is to demonstrate how the problems can be reduced of thistransportation area based on the works done by different researchers and companies. And I have also provided some ideas and comments according to those works and trying to provide some proposal to use better inspection method where it is needed.The scope of this thesis work is automatic interpretation of data from NDT, with the goal of detecting flaws accurately and efficiently. AI techniques such as neural networks, machine vision, knowledge-based systems and fuzzy logic were applied to a wide spectrum of problems in this area. Another scope is to provide an insight into possible research methods concerning railway sleeper, fastener, ballast and overhead inspection by automatic interpretation of data.In this thesis work, I have discussed about problems which are arise in railway sleepers,fastener, and overhead and ballasted track. For this reason I have reviewed some research papers related with these areas and demonstrated how their systems works and the results of those systems. After all the demonstrations were taking place of the advantages of using AI techniques in contrast with those manual systems exist previously.This work aims to summarize the findings of a large number of research papers deploying artificial intelligence (AI) techniques for the automatic interpretation of data from nondestructive testing (NDT). Problems in rail transport domain are mainly discussed in this work. The overall work of this paper goes to the inspection of railway sleepers, fastener, ballast and overhead.

This study develops a reliable intelligent non-destructive evaluation (NDE) expert system for carbon-carbon (C/C) composites based on thermography, ultrasonic, computed tomography and post processing by means of fuzzy expert system technique. Data features and NDE expert knowledge are seamlessly combined in the intelligent system to provide the best possible diagnosis of the potential defects and problems. As a result, this research help ensure C/C composites' integrity and reliability. Four types of orthotropic aerospace composite material groups, which include 2-D pitched based commercial aircraft disc brakes and asmolds, 3-D PAN based C/C composites, and carbon fiber reinforced plastic (CFRP) panels, were tested. Based on the performance testing results of thermography, air-coupled ultrasonic, and x-ray computed tomography, the testing data pattern corresponding to feature and quantification of defects were found. This NDE knowledge databases were transformed to fuzzy logic expert system models. The models succeefully classified and indicated the defect's size and distribution and the intelligent systems perform NDE better than human operators. These fuzzy expert systems not only eliminate human errors in defect detection but also function as NDE experts. In addition, fuzzy expert systems improve the defect detection by incorporating fuzzy expert rules to remove noises and to measure defect size more accurately. In the future, the expert system model could be continuously updated and modified to quantify the size and distribution of defects. The systems developed here can be adapted and applied to build an intelligent NDE expert system for better quality control as well as automatic defect and porosity detection in C/C composite production process.

<p>Cilj ovog istraživanja bio je da se razvije metoda čijim kori&scaron;ćenjem može da se doprinese pobolj&scaron;anju efikasnosti rada poljoprivrednih biogas postrojenja. Da bi se to ostvarilo, izabrani su odgovarajući kriterijumi i parametri. Razvijena je metoda za ocenu efikasnosti, a rezultati dobijeni njenim kori&scaron;ćenjem ukazuju na potrebu za pobolj&scaron;anjem. Razvijena je i metoda za analizu mogućnosti pobolj&scaron;anja, kojom se predlažu mere za ostvarenje pobolj&scaron;anja. U razvoju metoda kori&scaron;ćeni su principi fazi logike i ekspertskih sistema, da se modeluje neodređenost u podacima i koristi ekspertsko znanje za ocenu. Testiranjem metoda, koristeći podatke o radu petnaest postrojenja u Bavarskoj, zaključeno je da su metode primenljive u obliku pomoćnog alata za sprovođenje pobolj&scaron;anja efikasnosti.</p> / <p>The aim of this investigation was to develop a method that can be used<br />for improving the efficiency of agricultural biogas plants. In order to<br />achieve this, appropriate criteria and parameters were selected. The<br />method for assessing the efficiency was developed, which results<br />indicate the need for improvement. The method for analyzing the<br />possibilities of improvement was developed as well, in order to propose<br />measures for improvement. In development of methods, principles of<br />fuzzy logic and expert systems were used, in order to model uncertainty<br />in the data and to use expert knowledge for assessment. According to<br />test results, using data of fifteen biogas plants in Bavaria, it was<br />concluded that methods can be used for efficiency improvement.</p>