scholarly journals Prognosis of a Wind Turbine Gearbox Bearing Using Supervised Machine Learning

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3092 ◽  
Author(s):  
Elasha ◽  
Shanbr ◽  
Li ◽  
Mba
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
High Speed ◽  
Large Scale ◽  
Remaining Useful Life ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Accurate Estimation ◽  
Wind Turbine Gearbox ◽  
Vibration Measurements

Deployment of large-scale wind turbines requires sophisticated operation and maintenance strategies to ensure the devices are safe, profitable and cost-effective. Prognostics aims to predict the remaining useful life (RUL) of physical systems based on condition measurements. Analyzing condition monitoring data, implementing diagnostic techniques and using machinery prognostic algorithms will bring about accurate estimation of the remaining life and possible failures that may occur. This paper proposes to combine two supervised machine learning techniques, namely, regression model and multilayer artificial neural network model, to predict the RUL of an operational wind turbine gearbox using vibration measurements. Root Mean Square (RMS), Kurtosis (KU) and Energy Index (EI) were analysed to define the bearing failure stages. The proposed methodology was evaluated through a case study involving vibration measurements of a high-speed shaft bearing used in a wind turbine gearbox.

scholarly journals Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2705 ◽  
Author(s):  
Xiaochuan Li ◽  
Faris Elasha ◽  
Suliman Shanbr ◽  
David Mba
Keyword(s):  
Machine Learning ◽  
Roller Bearing ◽  
Remaining Useful Life ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Accurate Estimation ◽  
Rolling Element Bearings ◽  
Wind Turbine Gearbox ◽  
Rolling Element ◽  
Useful Life

Components of rotating machines, such as shafts, bearings and gears are subject to performance degradation, which if left unattended could lead to failure or breakdown of the entire system. Analyzing condition monitoring data, implementing diagnostic techniques and using machinery prognostic algorithms will bring about accurate estimation of the remaining life and possible failures that may occur. This paper proposes a combination of two supervised machine learning techniques; namely, the regression model and multilayer artificial neural network model, to predict the remaining useful life of rolling element bearings. Root mean square and Kurtosis were analyzed to define the bearing failure stages. The proposed methodology was validated through two case studies involving vibration measurements of an operational wind turbine gearbox and a split cylindrical roller bearing in a test rig.

Fault prognostics of rolling element bearing based on feature extraction and supervised machine learning: Application to shaft wind turbine gearbox using vibration signal

2020 ◽  
pp. 095440622097615
Author(s):  
Fawzi Gougam ◽  
Rahmoune Chemseddine ◽  
Djamel Benazzouz ◽  
Khaled Benaggoune ◽  
Noureddine Zerhouni
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
Energy Demand ◽  
High Speed ◽  
Remaining Useful Life ◽  
Rolling Element Bearing ◽  
Supervised Machine Learning ◽  
Electricity Production ◽  
Inference System ◽  
Fault Prognostics

Renewable energies offer new solutions to an ever-increasing energy demand. Wind energy is one of the main sources of electricity production, which uses winds to be converted to electrical energy with lower cost and environment saving. The major failures of a wind turbine occur in the bearings of high-speed shafts. This paper proposes the use of optimized machine learning to predict the Remaining Useful Life (RUL) of bearing based on vibration data and features extraction. Significant features are extracted from filtered band-pass of the squared raw signal where the health indicators are automatically selected using relief technique. Optimized Adaptive Neuro Fuzzy Inference System (ANFIS) by Partical Swarm Optimization (PSO) is used to model the non linear degradation of the extracted indicators. The proposed approach is applied on experimental setup of wind turbine where the results show its effectiveness for RUL estimation.

scholarly journals Wind turbine gearbox failure and remaining useful life prediction using machine learning techniques

Wind Energy ◽  
2018 ◽  
Vol 22 (3) ◽  
pp. 360-375 ◽  
Author(s):  
James Carroll ◽  
Sofia Koukoura ◽  
Alasdair McDonald ◽  
Anastasis Charalambous ◽  
Stephan Weiss ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Machine Learning Techniques ◽  
Wind Turbine Gearbox ◽  
Learning Techniques ◽  
Useful Life

scholarly journals Big data and democratic speech: Predicting deliberative quality using machine learning techniques

2021 ◽  
Vol 14 (2) ◽  
pp. 205979912110104
Author(s):  
Eleonore Fournier-Tombs ◽  
Michael K. MacKenzie
Keyword(s):  
Machine Learning ◽  
Speech Acts ◽  
Large Scale ◽  
Large Datasets ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Computational Techniques ◽  
Learning Techniques ◽  
Campaign Speeches ◽  
Northern Territories

This article explores techniques for using supervised machine learning to study discourse quality in large datasets. We explain and illustrate the computational techniques that we have developed to facilitate a large-scale study of deliberative quality in Canada’s three northern territories: Yukon, Northwest Territories, and Nunavut. This larger study involves conducting comparative analyses of hundreds of thousands of parliamentary speech acts since the creation of Nunavut 20 years ago. Without computational techniques, we would be unable to conduct such an ambitious and comprehensive analysis of deliberative quality. The purpose of this article is to demonstrate the machine learning techniques that we have developed with the hope that they might be used and improved by other communications scholars who are interested in conducting textual analyses using large datasets. Other possible applications of these techniques might include analyses of campaign speeches, party platforms, legislation, judicial rulings, online comments, newspaper articles, and television or radio commentaries.

scholarly journals Prognosis of Wind Turbine Gearbox Bearing Failures using SCADA and Modeled Data

2020 ◽  
Vol 12 (1) ◽  
pp. 10
Author(s):  
Arch Desai ◽  
Yi Guo ◽  
Shawn Sheng ◽  
Shawn Sheng ◽  
Caleb Phillips ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
Power Plants ◽  
Performance Enhancement ◽  
Low Frequency ◽  
Machine Learning Techniques ◽  
False Alarms ◽  
Bearing Failure ◽  
Wind Turbine Gearbox ◽  
Unseen Data

Predictive maintenance and condition monitoring systems for wind turbines have seen increased adoption to minimize downtime, reducing operation and maintenance costs. On today’s wind power plants, the integrated supervisory control and data acquisition (SCADA) system provides low- frequency operational data that can be leveraged to quantify a wind turbine’s health. The aim of this study is to utilize machine-learning techniques to predict axial cracking failures in wind turbine gearbox bearings up to 1 month ahead of time. The failures are assumed to have occurred when the investigated bearing was replaced. While current SCADA systems show the overall condition of a wind turbine, often they do not allow for the investigation of specific gearbox bearings’ health. To enrich bearing fault signatures, additional data are computed through physics-based models using gearbox design information. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score. Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision and F1 score is about 33% and 12% respectively, based on the best modeling case in this study.

scholarly journals COVID-19 Forecasting using Multivariate Linear Regression

2020 ◽  
Author(s):  
R. Suganya ◽  
R.Arunadevi ◽  
Seyed M.Buhari
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Large Scale ◽  
Learning Algorithms ◽  
Multivariate Linear Regression ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Error Matrix ◽  
The World

Abstract Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe respiratory syndrome coronavirus 2 (SARS-CoV-2). It was first identified in December 2019 in Wuhan, the capital of China’s Hubei province. The objective of this research is to propose a forecasting model using the COVID-19 available dataset from top affected regions across the world using machine learning algorithms. Machine Learning algorithms help us achieve this objective. Regression models are one of the supervised machine learning techniques to classify large-scale data. This research aims to apply Multivariate Linear Regression to predict the number of confirmed and death COVID-19 cases for a span of one and two weeks. The experimental results explain 99\% variability in prediction with the R-squared statistics scores of 0.992. The algorithms are evaluated using the error matrix such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and accuracy for top affected regions across the world.

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

2020 ◽  
Vol 28 (2) ◽  
pp. 253-265 ◽  
Author(s):  
Gabriela Bitencourt-Ferreira ◽  
Amauri Duarte da Silva ◽  
Walter Filgueira de Azevedo
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Predictive Performance ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Scoring Functions ◽  
Cyclin Dependent Kinase ◽  
Learning Models ◽  
Learning Techniques ◽  
Machine Learning Models

Background: The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities. Objective: Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures. Methods: We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models. Results: Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data. Conclusion: Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

scholarly journals Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques

2019 ◽  
Vol 23 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Shikha N. Khera ◽  
Divya
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Predictive Modelling ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
It Industry ◽  
Knowledge Based ◽  
Employee Attrition

Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.

scholarly journals Local mortality estimates during the COVID-19 pandemic in Italy

2021 ◽  
Author(s):  
Augusto Cerqua ◽  
Roberta Di Stefano ◽  
Marco Letta ◽  
Sara Miccoli
Keyword(s):  
Machine Learning ◽  
Excess Mortality ◽  
Control Method ◽  
Local Level ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Mortality Data ◽  
Official Method ◽  
Learning Techniques ◽  
Mortality Estimates

AbstractEstimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

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