Computer vision-based condition monitoring methods, the methods are increasingly used on railway systems. Rail condition monitoring process can be performed using data obtained with the help of computers using these methods. In this study, a computer-based visual rail condition monitoring is proposed. By means of a camera placed on top of the train the rail that the train is on and the neighbor rail images are taken. On these images, the edge and feature extraction methods are applied to determine the rails. The resulting several faults between railways were studied to determine if there is a failure. The results obtained are given at the end of the study. Experimental results show that the proposed method is examined, it is observed that a healthy and effective results.  Index Terms-condition monitoring, railway systems, image processing, fault diagnosis interests are image processing, fault diagnosis, computer vision, railway inspection systems and fuzzy systems.

Photovoltaic (PV) systems are the most commonly used methods. PV systems have the most potential and efficiency between the species of renewable energy. However, this method can be subjected to a loss of energy gain when shading occurs on the PV panels. To overcome this negative situation, a reconfiguration process in the PV systems has been developed. Through this method, the power losses caused by the shading of the panel on the system are attempting to be minimized . Without any change in the physical location of the pre-shading panels, the panels are co nfigured with electrical connectors. In this study, a new efficient and intelligent reconfiguration method is proposed. The short-circuit current values of the PV panels are detected using current sensors. Shortcircuit current values vary in proportion to the shadings that occur on the panels. A fuzzy calculation is carried out taking the current values into account as the system input parameters. The optimal connection structure is detected by using the fuzzy output values. The result is then applied to the system by switching the matrix circuit. The proposed method has been tested in different shadowing conditions and has been seen as a gain ratio. Experiments performed in real-time availability have confirmed the suitability of the method for different sized systems.

Computer vision-based tracking and fault detection methods are increasingly growing method for use on railway systems. These methods make detection of components of the railways and fault detection and condition monitoring process can be performed using data obtained by means of computers. In this study, methods are proposed for fault detection on railway components and condition monitoring. With cameras placed on the bottom and the top of the experimental vehicle the images are taken. The camera at the top, overhead rails are positioned to see a way for war and the camera is fixed to the bottom mounted to see clearly railway components. Images from cameras placed on the bottom, Canny edge extraction and Hough transform methods are applied. The types of the components and faults are determined by using classification algorithm with decision trees using the obtained data. The condition monitoring has done by the camera is positioned on the upper part of the vehicle. By processing the taken images with processing methods, inclination angle of the rails and direction of railways are detected. Thus, during the course of the vehicle is obtained information of the direction of railway. Real images are used in the operation of railways belonging to the experimental environment. On these images, to identify the components of the proposed method using the railways and rail direction determination is made. The results obtained are given at the end of the study. The experimental results are analyzed, it is observed that the proposed method accurate and effective results.

No abstract

No abstract