One of the most important assets in an industry would be rotating machines. The reliability and availability are very crucial in order to support the accomplishment of an industry field. Major and even minor faults in rotating machines cause a decrease in both productivity and cost efficiency. Various methods have been studied by researcher and introduced in the industry for the detection of an early fault in rotating machines. Vibration signal analysis is one of a standout amongst other methods. This research paper focused on early fault detection in the bearing component at two different positions; inner raceway and ball. The faults were established at three different diameters of 0.007 inches, 0.021 inches, and 0.028 inches. By utilizing time domain technique, parameters such as mean, median, standard deviation, RMS, skewness, impulse factor and shape factor were determined. The vibration signal for both healthy and faulty bearing was deliberated by using the MATLAB software. All the data obtained were represented in graphs where the healthy and faulty bearing values were compared and analyzed.
<p>The prediction of tropospheric ozone concentrations is very important due to negative effects of ozone on human health, atmosphere and vegetation. Ozone Prediction is an intricate procedure and most of the conventional models cannot provide accurate prediction. Machine Learning techniques have been widely used as an effective tool for prediction. This study is investigating the implementation of Support vector Machine-SVM to predict Ozone concentrations. The results show that the SVM is capable in predicting ozone concentrations with acceptable level of accuracy. Sensitivity analysis has been conducted to show what is the most effective parameters on the proposed model<em>.</em></p>
<span>The solar radiation prediction in Kuala Terengganu located in Terengganu, Malaysia was investigated in this study to improve the solar system design. Solar radiation data and number of parameters such as solar radiation, temperature, humidity, wind speed and sunshine hours were obtained from Malaysian Meteorological Malaysia MMD. In order to predict the solar radiation, Genetic Programming Techniques (GP) models were develop and tested. Two scenarios were considered in this study in order to validate the efficiency of the proposed model. Coefficients of determination (R2) for the solar radiation during training and testing phases were ranged between 0.99402 to 0.98934 for all months of the year. This study confirms the ability of GP to predict solar radiation values precisely and accurately. The predictions from the GP models could enable scientists to locate <br /> and design solar energy systems in Malaysia.</span>
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