Diagnostic Models for Wind Turbine Gearbox Components Using SCADA Time Series Data

2018 ◽  
Author(s):  
Rafael Orozco ◽  
Shuangwen Sheng ◽  
Caleb Phillips
Keyword(s):  
Time Series ◽  
Wind Turbine ◽  
Time Series Data ◽  
Series Data ◽  
Wind Turbine Gearbox ◽  
Diagnostic Models

scholarly journals Development of a wind turbine gearbox virtual load sensor using multibody simulation and artificial neural networks

2021 ◽  
Author(s):  
Baher Azzam ◽  
Ralf Schelenz ◽  
Björn Roscher ◽  
Abdul Baseer ◽  
Georg Jacobs
Keyword(s):  
Time Series ◽  
Wind Turbine ◽  
High Speed ◽  
Time Series Data ◽  
Development Trend ◽  
Series Data ◽  
Ann Model ◽  
Wind Turbine Gearbox ◽  
Virtual Sensor ◽  
Artificial Neural

AbstractA current development trend in wind energy is characterized by the installation of wind turbines (WT) with increasing rated power output. Higher towers and larger rotor diameters increase rated power leading to an intensification of the load situation on the drive train and the main gearbox. However, current main gearbox condition monitoring systems (CMS) do not record the 6‑degree of freedom (6-DOF) input loads to the transmission as it is too expensive. Therefore, this investigation aims to present an approach to develop and validate a low-cost virtual sensor for measuring the input loads of a WT main gearbox. A prototype of the virtual sensor system was developed in a virtual environment using a multi-body simulation (MBS) model of a WT drivetrain and artificial neural network (ANN) models. Simulated wind fields according to IEC 61400‑1 covering a variety of wind speeds were generated and applied to a MBS model of a Vestas V52 wind turbine. The turbine contains a high-speed drivetrain with 4‑points bearing suspension, a common drivetrain configuration. The simulation was used to generate time-series data of the target and input parameters for the virtual sensor algorithm, an ANN model. After the ANN was trained using the time-series data collected from the MBS, the developed virtual sensor algorithm was tested by comparing the estimated 6‑DOF transmission input loads from the ANN to the simulated 6‑DOF transmission input loads from the MBS. The results show high potential for virtual sensing 6‑DOF wind turbine transmission input loads using the presented method.

scholarly journals Early stage white etching crack identification using artificial neural networks

2021 ◽  
Author(s):  
Xiaodi Liu ◽  
Baher Azzam ◽  
Freia Harzendorf ◽  
Johann Kolb ◽  
Ralf Schelenz ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Turbine ◽  
Time Series Data ◽  
Early Stage ◽  
Reconstruction Error ◽  
Sensor Data ◽  
Series Data ◽  
Crack Identification ◽  
Testing Time ◽  
Premature Failure

AbstractWhite Etching Cracks (WEC) in gearbox bearings is a major concern in the wind turbine industry, which can lead to a premature failure of the gearbox. Though many hypotheses regarding the generation of WEC have been proposed over the decades, the answer is still disputable. To trace back the failures to earlier stages before they occur, an innovative sensor-set has been utilized on a test rig to monitor the influencing factors that lead to WEC. This paperwork seeks to recognize abnormal patterns from recorded sensor data and derive statements of sensible sensor combinations in WEC early detection. A Long Short Term Memory (LSTM) network-based autoencoder is proposed for the anomaly detection (AD) task. Employing an auto-associative sequence-to-sequence predictor, a model is trained to reconstruct the normal time series data without WEC. The reconstruction error of testing time series data is evaluated for the determination of its anomaly. The results show that the specified LSTM autoencoder framework can qualitatively distinguish anomalies from collected multivariate time series data. Moreover, the anomaly score evaluated via reconstruction-error-based metrics can discriminate normal and abnormal behaviors in the study. This investigation’s results entail a significant step towards early WEC risk detection and more cost-efficient wind turbine technology if this approach can be further applied on stream data with plausible thresholds in monitoring system.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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