The vibrations of bearings holding the high-speed shaft of a steam turbine are critically controlled for the safe and reliable power generation at the power plants. In this paper, two artificial intelligence (AI) process models, i.e., artificial neural network (ANN) and support vector machine (SVM) based relative vibration modeling of a steam turbine shaft bearing of a 660 MW supercritical steam turbine system is presented. After extensive data processing and machine learning based visualization tests performed on the raw operational data, ANN and SVM models are trained, validated and compared by external validation tests. ANN has outperformed SVM in terms of better prediction capability and is, therefore, deployed for simulating the constructed operating scenarios. ANN process model is tested for the complete load range of power plant, i.e., from 353 MW to 662 MW and 4.07% reduction in the relative vibration of the bearing is predicted by the network. Further, various vibration reduction operating strategies are developed and tested on the validated and robust ANN process model. A selected operating strategy which has predicted a promising reduction in the relative vibration of bearing is selected. In order to confirm the effectiveness of the prediction of the ANN process model, the selected operating strategy is implemented on the actual operation of the power plant. The resulting reduction in the relative vibrations of the turbine's bearing, which is less than the alarm limit, are confirmed. This cements the role of ANN process model to be used as an operational excellence tool resulting in vibration reduction of high-speed rotating equipment. (c) 2021 THE AUTHORS. Production and hosting by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Vehicle Exterior Body Damage(VEBD) parts classification is important for claim processing, cost estimation, accident investigation, and vehicle damage assessment. Class imbalance in the VEBD part classification dataset is a primary factor affecting the classification performance of existing classification models. Although the availability of datasets and the system's capability significantly impact system performance, its damage part classification is still limited due to its dynamic body structure, size, shape, color, and types of damage. In this paper, we propose a novel heterogeneous ensemble learning (HEL) model based on VEBD data (VEBDHEL) to deal with imbalanced data in VEBD. We validate the effectiveness of VEBD-HEL on two original and generated VEBD datasets. The experimental results demonstrate that compared with current state-of-the-art models, VEBD-HEL has the best comprehensive performance. The proposed model not only achieves good Accuracy (99.93%) . 93%) rates for both the simple damage and the severe damage but also increases the Area Under Curve (AUC) to 99.83%.
Latent heat thermal storage (LHTS) system is vital to reduce environment pollution. In the shell-and-tube heat accumulator, the position of the inner heating tube plays a vital role in the thermal storage. To analyze the effect of the inner tube position on the phase transition, a two-dimensional numerical model is developed. The structure has the minimum full melting time of 3480 s when the inner tube is 12 mm (L = 12 mm) from the center. Compared with L = 0 mm, the full melting time at L = 12 mm can be reduced by 13.4%.