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In this study, it was achieved by using the method of impulse noise to detect internal or surface cracks that can occur in the production of ceramic plates. Ceramic materials are often used in the industry, especially as kitchenware and in areas such as the construction sector. Many different methods are used in the quality assurance processes of ceramic materials. In this study, the impact noise method was examined. This method is a test technique that was not used in applications. The method is presented as an examination technique based on whether there is a deformation on the material according to the sound coming from it as a result of a plastic bit hammer impact on the ceramic material. The application of the study was performed on plates made of ceramic materials. Here, it was made with the same type of model plates manufactured from the same material. The noise that would occur as a result of the impact applied on a point determined on the materials to be tested has been examined by the method of time-frequency analysis. The method applied gives pretty good results for distinguishing ceramic plates in good condition from those which are cracked.

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This study presents our findings on the ferroresonance phenomenon for the Seyitomer-Isiklar part of the Electric Power System of 380 kV in Turkey. In this context, the power spectral density approach and Short-Time Fourier Transforms are applied to the voltage variations for phase R of the sample power network. The findings show that the ferroresonance event can be represented by the interharmonics between 50 and 250 Hz, as well as statistical properties. In addition, a histogram of the ferroresonance region shows a different characteristic from the overall data and the nonferroresonance region. This different characteristic can be defined by an asymmetrical distribution characteristic, and its numerical measure is given by the "Noise to Signal Ratio" of 0.624.

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In this study, an artificial neural network application was performed to tell if 18 plates of the same material in different shapes and sizes were cracked or not. The cracks in the cracked plates were of different depth and sizes and were non-identical deformations. This ANN model was developed to detect whether the plates under test are cracked or not, when four plates have been selected randomly from among a total of 18 ones. The ANN model used in the study is a model uniquely tailored for this study, but it can be applied to all systems by changing the weight values and without changing the architecture of the model. The developed model was tested using experimental data conducted with 18 plates and the results obtained mainly correspond to this particular case. But the algorithm can be easily generalized for an arbitrary number of items.

The operation of energy transmission lines with high efficiency without failure has great importance in today's electricity-dependent world. Problems that may occur in electricity transmission lines are failure cause of many operations not only industrial but also daily life. One of the most important causes of the problems encountered in power lines is the change in the amount of sagging. The change of sagging amount causes line breaks and losing energy efficiency. This problem, which is frequently encountered due to seasonal and climatic changes, is one of the major problems of continuity in the power line. The calculation of sag contains uncertain and variable parameters that can change seasonally, climatically and/or structurally such as weight per unit length of the conductor, the horizontal component of tension, total tension, etc. In this case, it is difficult to calculate a precise and reliable sag amount. The sagging of power lines is generally calculated theoretically or measured on-site by the personnel in charge. In this study, a new approach is presented to measure the sag amount by using sensor data of a power line inspection robot, precisely and reliably. The inspection robot moving on the power line can be remotely controlled and send sensor data. The sagging is measured with an error of less than 2 percent in the laboratory test field by using this technique.

Artificial neural network models were used for short term wind speed forecasting in the Mardin area, located in the Southeast Anatolia region of Turkey. Using data that was obtained from the State Meteorological Service and that encompassed a ten year period, short term wind speed forecasting for the Mardin area was performed. A number of different ANN models were developed in this study. The model with 60 neurons is the most successful model for short term wind speed forecasting. The mean squared error and approximation values for training of this model were 0.378088 and 0.970490, respectively. The ANN models developed in the study have produced satisfactory results. The most successful among those models constitutes a model that can be used by the Mardin Electric Utility Control Centre.