ANALYSIS & STUDY OF AI TECHNIQUES FORAUTOMATIC CONDITION MONITORING OFRAILWAY TRACK INFRASTRUCTURE : Artificial Intelligence Techniques

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.

Evaluation of a PVT Air Collector

Hybrid Photovoltaic Thermal (PVT) collectors are an emerging technology that combines PV and solar thermal systems in a single solar collector producing heat and electricity simultaneously. The focus of this thesis work is to evaluate the performance of unglazed open loop PVT air system integrated on a garage roof in Borlänge. As it is thought to have a significant potential for preheating ventilation of the building and improving the PV modules electrical efficiency. The performance evaluation is important to optimize the cooling strategy of the collector in order to enhance its electrical efficiency and maximize the production of thermal energy. The evaluation process involves monitoring the electrical and thermal energies for a certain period of time and investigating the cooling effect on the performance through controlling the air mass flow provided by a variable speed fan connected to the collector by an air distribution duct. The distribution duct transfers the heated outlet air from the collector to inside the building. The PVT air collector consists of 34 Solibro CIGS type PV modules (115 Wp for each module) which are roof integrated and have replaced the traditional roof material. The collector is oriented toward the south-west with a tilt of 29 ᵒ. The collector consists of 17 parallel air ducts formed between the PV modules and the insulated roof surface. Each air duct has a depth of 0.05 m, length of 2.38 m and width of 2.38 m. The air ducts are connected to each other through holes. The monitoring system is based on using T-type thermocouples to measure the relevant temperatures, air sensor to measure the air mass flow. These parameters are needed to calculate the thermal energy. The monitoring system contains also voltage dividers to measure the PV modules voltage and shunt resistance to measure the PV current, and AC energy meters which are needed to calculate the produced electrical energy. All signals recorded from the thermocouples, voltage dividers and shunt resistances are connected to data loggers. The strategy of cooling in this work was based on switching the fan on, only when the difference between the air duct temperature (under the middle of top of PV column) and the room temperature becomes higher than 5 °C. This strategy was effective in term of avoiding high electrical consumption by the fan, and it is recommended for further development. The temperature difference of 5 °C is the minimum value to compensate the heat losses in the collecting duct and distribution duct. The PVT air collector has an area of (Ac=32 m2), and air mass flow of 0.002 kg/s m2. The nominal output power of the collector is 4 kWppv (34 CIGS modules with 115 Wppvfor each module). The collector produces thermal output energy of 6.88 kWth/day (0.21 kWth/m2 day) and an electrical output energy of 13.46 kWhel/day (0.42 kWhel/m2 day) with cooling case. The PVT air collector has a daily thermal energy yield of 1.72 kWhth/kWppv, and a daily PV electrical energy yield of 3.36 kWhel /kWppv. The fan energy requirement in this case was 0.18 kWh/day which is very small compared to the electrical energy generated by the PV collector. The obtained thermal efficiency was 8 % which is small compared to the results reported in literature for PVT air collectors. The small thermal efficiency was due to small operating air mass flow. Therefore, the study suggests increasing the air mass flow by a factor of 25. The electrical efficiency was fluctuating around 14 %, which is higher than the theoretical efficiency of the PV modules, and this discrepancy was due to the poor method of recording the solar irradiance in the location. Due to shading effect, it was better to use more than one pyranometer.