Cable aging is one of the main security risks to power systems. With the widely used cables in power systems, the accurate assessment of cable aging status is increasingly important. This study proposes an efficient a...
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Cable aging is one of the main security risks to power systems. With the widely used cables in power systems, the accurate assessment of cable aging status is increasingly important. This study proposes an efficient assessment model based on the PSO-XGBoost algorithm, which integrates the particle swarm optimization (PSO) algorithm and the extremegradientboosting (XGBoost) algorithm. The XGBoost model is established to assess the cable aging status with the inputs of partial discharge, operating life, corrosion condition and load condition. The PSO algorithm automatically optimizes parameters during XGBoost model training. Then, the standard performance evaluation metrics of the proposed assessment model are compared with four advanced classification models. The accuracy, precision, recall and F1-score of the assessment model are above 98%, indicating that the proposed PSO-XGBoost model can accurately assess the cable aging state. Furthermore, these calculation results of the proposed model are better than the other four benchmark models, which shows that the proposed model performs better in cable aging status assessment than the existing models.
The α-β Ti-6Al-4V (Ti64) alloy possesses high specific strength and excellent corrosion resistance properties, established its utilization in aerospace and biomedical applications. The additively manufactured Ti64 a...
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The α-β Ti-6Al-4V (Ti64) alloy possesses high specific strength and excellent corrosion resistance properties, established its utilization in aerospace and biomedical applications. The additively manufactured Ti64 alloy has shown its rapid growth in the recent years because of its design flexibility and manufacturing efficiency. As this Ti alloy is used in aerospace and bio-medical applications, it experiences variable loading conditions during actual service conditions and therefore, it is essential to estimate its fatigue life. The fatigue experimentations are highly expensive and time-consuming procedure to evaluate the life of the component, especially in the case of Ti alloys which is costly. Hence, destructive testing of this alloy is not desirable to estimate its fatigue life. To overcome these limitations, efficient methodology is required to predict the cyclic life of Ti alloy quickly and economically. Machine learning (ML) technique is one of the feasible solutions to fulfill such requirements and predict fatigue life using experimental data reported in the literature. Keeping this in view, extremegradientboosting (XGB) and Random Forest (RF) model have been used to predict the fatigue life of Ti alloys manufactured through laser powder bed fusion (LPBF). These XGB and RF have been trained using fatigue data of Ti alloy, which is influenced by process variables namely, Energy Density during manufacturing and Stress Amplitude during testing. The collected fatigue data is split in to train (80%) and test data (20%) and the XGB and RF are trained using the former and its predictive accuracy is estimated with the quantification of error. Subsequently, the trained XGB and RF are used to analyse the test data. The accuracy of XGB and RF for predictability of fatigue life of Ti alloy is assessed by using mean squared error and R 2 scores.
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