A wind turbine works under variable load and environmental conditions because of which failure rate has been on the rise. Failure of a gearbox, an integral part of producing wind energy, contributes to 80 % of the total downtime for the wind turbine. For ensuring better utilization of the wind turbines, Fault prognosis and condition monitoring of bearings are of utmost importance as it helps to reduce the downtime by early detection of faults which further increases the power output. In this paper, vibration signals produced and machine learning approach to determine the Remaining Useful Life (RUL) for a degraded bearing is studied. The methodology includes statistical feature extraction analysis with regression models. Further the feature selection is done using Principal Component Analysis (PCA) technique which produces training and testing sets which acts as an input parameter for regression models such as Support Vector Regressor (SVR) and Random Forest (RF). Weibull Hazard Rate Function is used for calculating the RUL of the bearing. Results This study shows the potential application of regression model as an effective tool for degradation performance prediction of bearing.
Child mortality is one of the important factors to reflect sustainable development for any nation. It is common sense now that child mortality rate depends on medical services platform and its quality. To predict model under-five child mortality rates in India, multiple linear regression analysis was used. Identifying the factors that affects the under-five child mortality. To examine the relationship between all the variables along with recognize the problem of multicollinearity in the variables. In this study our fitted multiple linear regression model found that under five child mortality rates in India are influenced by tuberculosis case detection rate, measles death rate and hepatitis immunization rate. To choose appropriate model ACI score is used.
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