Wind turbines are widely installed as the new source of cleaner energy production. Dynamic and random stress imposed on the generator bearing of a wind turbine may lead to overheating and failure. In this paper, a data-driven approach for condition monitoring of generator bearings using temporal temperature data is presented. Four algorithms, the support vector regression machine, neural network, extreme learning machine, and the deep belief network are applied to model the bearing behavior. Comparative analysis of the models has demonstrated that the deep belief network is most accurate. It has been observed that the bearing failure is preceded by a change in the prediction error of bearing temperature. An exponentially-weighted moving average (EWMA) control chart is deployed to trend the error. Then a binary vector containing the abnormal errors and the normal residuals are generated for classifying failures. LS-SVM based classification models are developed to classify the fault bearings and the normal ones. The proposed approach has been validated with the data collected from 11 wind turbines.

To maximize energy extraction, the nacelle of a wind turbine follows the wind direction. Accurate prediction of wind direction is vital for yaw control. A tandem hybrid approach to improve the prediction accuracy of the wind direction data is developed. The proposed approach in this paper includes the bilinear transformation, effective data decomposition techniques, long-short-term-memory recurrent neural networks (LSTM-RNNs), and error decomposition correction methods. In the proposed approach, the angular wind direction data is firstly transformed into time-series to accommodate the full range of yaw motion. Then, the continuous transformed series are decomposed into a group of subseries using a novel decomposition technique. Next, for each subseries, the wind directions are predicted using LSTM-RNNs. In the final step, it decomposed the errors for each predicted subseries to correct the predicted wind direction and then perform inverse bilinear transformation to obtain the final wind direction forecasting. The robustness and effectiveness of the proposed approach are verified using data collected from a wind farm located in Huitengxile, Inner Mongolia, China. Computational results indicate that the proposed hybrid approach outperforms the other single approaches tested to predict the nacelle direction over short-time horizons. The proposed approach can be useful for practical wind farm operations.

Increased concentrations of nitrogenous compounds in stream networks are detrimental to the health of both humans and ecosystems. Monitoring, modeling, and forecasting nitrate concentration in the temporal domain are essential for an in-depth understanding of nitrate dynamics and transformation within stream networks. In this study, an advanced chaotic modeling and forecasting approach integrated with turning point analysis is proposed. First, the time-series daily nitrate concentrations in the form of nitrate-nitrite were reconstructed based on the chaotic characteristics and then input into the forecasting models. Second, an echo state network (ESN) was developed for one-day-ahead nitrate concentration forecasting, and the hyperparameters were optimized through an improved flower pollination algorithm (IFPA) to achieve a high efficiency. Furthermore, turning point analysis was performed to quantify the relationship between discharge and peak nitrate concentration. The Ricker function was fitted, and the parameters were estimated for turning points using the forecasted nitrate concentration and measured discharge. Field data, including daily stream nitrate concentration and information on discharge collected from eight different monitoring sites in the southern Sichuan Basin, China, were utilized for case studies. A comparative analysis was performed under three modeling scenarios, viz. conventional time-series modeling, temporal signal decomposition, and data reconstruction and embedding with chaotic characteristics. Four benchmark time-series forecasting algorithms were compared against the proposed IFPA-ESN in the above-mentioned scenarios. For each site, parameters of the Ricker functions were estimated, and turning points were computed based on the forecasted nitrate concentration and discharge. Computational results validated the superiority of the proposed approach in improving the accuracy of stream nitrate concentration prediction. The limitations to the supply and transportation of nitrogenous compounds were quantified, which would be valuable for pollution mitigation in the future.

This pioneering research introduces a novel approach for decision-makers in the heavy machinery industry, specifically focusing on production management. The study integrates machine learning techniques like Ridge Regression, Markov chain analysis, and radar charts to optimize North American Crawler Cranes market production processes. Ridge Regression enables growth pattern identification and performance assessment, facilitating comparisons and addressing industry challenges. Markov chain analysis evaluates risk factors, aiding in informed decision-making and risk management. Radar charts simulate benchmark product designs, enabling data-driven decisions for production optimization. This interdisciplinary approach equips decision-makers with transformative insights, enhancing competitiveness in the heavy machinery industry and beyond. By leveraging these techniques, companies can revolutionize their production management strategies, driving success in diverse markets.