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ANALYSIS & STUDY OF AI TECHNIQUES FOR AUTOMATIC CONDITION MONITORING OF RAILWAY TRACK INFRASTRUCTURE: Artificial Intelligence Techniques
Dalarna University, School of Technology and Business Studies, Computer Engineering.
2010 (English)Independent thesis Advanced level (degree of Master (Two Years))Student thesis
Abstract [en]

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 like machine 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 this transportation 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.

Place, publisher, year, edition, pages
Borlange, Dalarna, 2010. , 70 p.
Keyword [en]
Artificial intelligence; Non-destructive testing; Condition Monitoring; Machine Vision; Neural Network; Genetic Algorithm; Fuzzy Logic; Expert System.
Identifiers
URN: urn:nbn:se:du-4757OAI: oai:dalea.du.se:4757DiVA: diva2:518859
Uppsok
Technology
Supervisors
Available from: 2010-05-31 Created: 2010-05-31 Last updated: 2012-04-24Bibliographically approved

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