MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS

The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection.The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. Manual or automated condition monitoring of materials in fields of public transportation like railway, aerial navigation, traffic safety, etc, where safety is of prior importance needs non-destructive testing (NDT).In general, wooden railway sleeper inspection is done manually by a human operator, by moving along the rail sleeper and gathering information by visual and sound analysis for examining the presence of cracks. Human inspectors working on lines visually inspect wooden rails to judge the quality of rail sleeper. In this project work the machine vision system is developed based on the manual visual analysis system, which uses digital cameras and image processing software to perform similar manual inspections. As the manual inspection requires much effort and is expected to be error prone sometimes and also appears difficult to discriminate even for a human operator by the frequent changes in inspected material. The machine vision system developed classifies the condition of material by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features.A pattern recognition approach is developed based on the methodological knowledge from manual procedure. The pattern recognition approach for this thesis work was developed and achieved by a non destructive testing method to identify the flaws in manually done condition monitoring of sleepers.In this method, a test vehicle is designed to capture sleeper images similar to visual inspection by human operator and the raw data for pattern recognition approach is provided from the captured images of the wooden sleepers. The data from the NDT method were further processed and appropriate features were extracted.The collection of data by the NDT method is to achieve high accuracy in reliable classification results. A key idea is to use the non supervised classifier based on the features extracted from the method to discriminate the condition of wooden sleepers in to either good or bad. Self organising map is used as classifier for the wooden sleeper classification.In order to achieve greater integration, the data collected by the machine vision system was made to interface with one another by a strategy called fusion. Data fusion was looked in at two different levels namely sensor-level fusion, feature- level fusion. As the goal was to reduce the accuracy of the human error on the rail sleeper classification as good or bad the results obtained by the feature-level fusion compared to that of the results of actual classification were satisfactory.

Energy Analysis within Industrial Hydraulics and Correspondent Solar PV System Design

Energy efficiency and renewable energy use are two main priorities leading to industrial sustainability nowadays according to European Steel Technology Platform (ESTP). Modernization efforts can be done by industries to improve energy consumptions of the production lines. These days, steel making industrial applications are energy and emission intensive. It was estimated that over the past years, energy consumption and corresponding CO2 generation has increased steadily reaching approximately 338.15 parts per million in august 2010 [1]. These kinds of facts and statistics have introduced a lot of room for improvement in energy efficiency for industrial applications through modernization and use of renewable energy sources such as solar Photovoltaic Systems (PV).The purpose of this thesis work is to make a preliminary design and simulation of the solar photovoltaic system which would attempt to cover the energy demand of the initial part of the pickling line hydraulic system at the SSAB steel plant. For this purpose, the energy consumptions of this hydraulic system would be studied and evaluated and a general analysis of the hydraulic and control components performance would be done which would yield a proper set of guidelines contributing towards future energy savings. The results of the energy efficiency analysis showed that the initial part of the pickling line hydraulic system worked with a low efficiency of 3.3%. Results of general analysis showed that hydraulic accumulators of 650 liter size should be used by the initial part pickling line system in combination with a one pump delivery of 100 l/min. Based on this, one PV system can deliver energy to an AC motor-pump set covering 17.6% of total energy and another PV system can supply a DC hydraulic pump substituting 26.7% of the demand. The first system used 290 m2 area of the roof and was sized as 40 kWp, the second used 109 m2 and was sized as 15.2 kWp. It was concluded that the reason for the low efficiency was the oversized design of the system. Incremental modernization efforts could help to improve the hydraulic system energy efficiency and make the design of the solar photovoltaic system realistically possible. Two types of PV systems where analyzed in the thesis work. A method was found calculating the load simulation sequence based on the energy efficiency studies to help in the PV system simulations. Hydraulic accumulators integrated into the pickling line worked as energy storage when being charged by the PV system as well.

Evaluation of a micro PV-Wind hybrid system in Nordic climate conditions

A one year data analysis for a micro PV-Wind hybrid system (0.52 kW + 1 kW), installed in Borlänge/Sweden is presented in this paper. The system performance was evaluated according the guidelines of the IEC 61724 standard. The parameters obtained allow a comparison with similar systems. The measurement data are also used to evaluate the sizing and operation of the hybrid system. In addition, the system was modelled in HOMER to study sizing options.