scholarly journals SCADA Data-Driven Wind Turbine Main Bearing Fault Prognosis Based on One-Class Support Vector Machines

2021 ◽  
Vol 19 ◽  
pp. 338-343
Author(s):  
A. Insuasty ◽  
◽  
C. Tutivén ◽  
Y. Vidal
Keyword(s):  
Wind Turbine ◽  
Data Driven ◽  
Support Vector ◽  
Main Bearing ◽  
Bearing Fault ◽  
Data Contamination ◽  
Vector Machines ◽  
Unsupervised Approach ◽  
Fault Prognosis ◽  
And Performance

This work proposes a fault prognosis methodology to predict the main bearing fault several months in advance and let turbine operators plan ahead. Reducing downtime is of paramount importance in wind energy industry to address its energy loss impact. The main advantages of the proposed methodology are the following ones. It is an unsupervised approach, thus it does not require faulty data to be trained; ii) it is based only on exogenous data and one representative temperature close to the subsystem to diagnose, thus avoiding data contamination; iii) it accomplishes the prognosis (various months in advance) of the main bearing fault; and iv) the validity and performance of the established methodology is demonstrated on a real underproduction wind turbine.

scholarly journals Wind Turbine Main Bearing Fault Prognosis Based Solely on SCADA Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2228 ◽  
Author(s):  
Ángel Encalada-Dávila ◽  
Bryan Puruncajas ◽  
Christian Tutivén ◽  
Yolanda Vidal
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Critical Issue ◽  
Main Bearing ◽  
Bearing Fault ◽  
Bearing Failures ◽  
Relevant Topic ◽  
Fault Prognosis ◽  
And Performance

As stated by the European Academy of Wind Energy (EAWE), the wind industry has identified main bearing failures as a critical issue in terms of increasing wind turbine reliability and availability. This is owing to major repairs with high replacement costs and long downtime periods associated with main bearing failures. Thus, the main bearing fault prognosis has become an economically relevant topic and is a technical challenge. In this work, a data-based methodology for fault prognosis is presented. The main contributions of this work are as follows: (i) Prognosis is achieved by using only supervisory control and data acquisition (SCADA) data, which is already available in all industrial-sized wind turbines; thus, no extra sensors that are designed for a specific purpose need to be installed. (ii) The proposed method only requires healthy data to be collected; thus, it can be applied to any wind farm even when no faulty data has been recorded. (iii) The proposed algorithm works under different and varying operating and environmental conditions. (iv) The validity and performance of the established methodology is demonstrated on a real underproduction wind farm consisting of 12 wind turbines. The obtained results show that advanced prognostic systems based solely on SCADA data can predict failures several months prior to their occurrence and allow wind turbine operators to plan their operations.

Support Vector Machines and Affective Science

2020 ◽  
Vol 12 (4) ◽  
pp. 297-308
Author(s):  
Chris H. Miller ◽  
Matthew D. Sacchet ◽  
Ian H. Gotlib
Keyword(s):  
Support Vector Machines ◽  
Reduction Technique ◽  
Feature Reduction ◽  
Data Driven ◽  
Future Research ◽  
Support Vector ◽  
Predictive Algorithm ◽  
Affective Science ◽  
Vector Machines ◽  
Potential Applications

Support vector machines (SVMs) are being used increasingly in affective science as a data-driven classification method and feature reduction technique. Whereas traditional statistical methods typically compare group averages on selected variables, SVMs use a predictive algorithm to learn multivariate patterns that optimally discriminate between groups. In this review, we provide a framework for understanding the methods of SVM-based analyses and summarize the findings of seminal studies that use SVMs for classification or data reduction in the behavioral and neural study of emotion and affective disorders. We conclude by discussing promising directions and potential applications of SVMs in future research in affective science.

scholarly journals A Big-Data-Driven Framework for Parking Demand Estimation in Urban Central Districts

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yunlin Guan ◽  
Yun Wang ◽  
Xuedong Yan ◽  
Haonan Guo ◽  
Yu Zhou
Keyword(s):  
Big Data ◽  
Clustering Algorithms ◽  
Demand Estimation ◽  
Accurate Estimate ◽  
Data Driven ◽  
Support Vector ◽  
Regression Problem ◽  
Vector Machines ◽  
Zone Division ◽  
Parking Demand

Parking planning is a key issue in the process of urban transportation planning. To formulate a high-quality planning scheme, an accurate estimate of the parking demand is critical. Most previous published studies were based primarily on parking survey data, which is both costly and inaccurate. Owing to limited data sources and simplified models, most of the previous research estimates the parking demand without consideration for the relationship between parking demand, land use, and traffic attributes, thereby causing a lack of accuracy. Thus, this study proposes a big-data-driven framework for parking demand estimation. The framework contains two steps. The first step is the parking zone division method, which is based on the statistical information grid and multidensity clustering algorithms. The second step is parking demand estimation, which is extracted by support vector machines posed in the form of a machine learning regression problem. The framework is evaluated using a case in the city center in Cangzhou, China.

scholarly journals ECG Based Biometric uing Wavelet Packet Decomposition

2019 ◽  
Vol 9 (1) ◽  
pp. 2178-2183 ◽  
Keyword(s):  
Discriminant Analysis ◽  
Wavelet Packet ◽  
Support Vector ◽  
Frequency Noise ◽  
Wavelet Packet Decomposition ◽  
Linear Discriminant ◽  
Biometric Technology ◽  
Vector Machines ◽  
And Performance ◽  
Electrocardiogram Ecg

Biometric technology has been commonly used for authentication. Fingerprint or iris become one of the biometrics that is widely applied. However, this type of biometrics tends to be easily falsified and damaged. So it is misused for manipulating actions and even crime. Therefore a new biometric method is needed to overcome this problem. One potential modality is biometrics based on an electrocardiogram (ECG) signal. This research simulates a one-lead ECG waveform for person authentication. ECG waves were taken from eleven healthy adult volunteers with a length of 60 seconds. ECG waves from each person are segmented into 10 sections so that a total of 110 ECG waves are used for person authentication simulations. All noise of the ECG waves was removed using a bandpass filter to reduce artifacts and high-frequency noise. Wavelet packet decomposition (3 Level) was applied to decompose the signal in several intrinsic parts so that typical wave information can be retrieved. Entropy-based feature extraction applied to all decomposed signals. A total of 14 entropy features have been calculated and used as predictors in the classification process. Validation and performance tests are carried out by cross-validation combined with linear discriminant analysis and support vector machines with five scenarios. The proposed method provides the highest accuracy of 71.8% using discriminant analysis and cubic support vector machine. The best accuracy value was achieved if all entropy features from all wavelet decomposition levels are used as predictors in the classification process. This research is expected to be a reference that ECG has the potential to become a future biometric modality

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