scholarly journals Efficient Threshold Monitoring for Distributed Probabilistic Data

2012 ◽  
Author(s):  
Mingwang Tang ◽  
Feifei Li ◽  
Jeff M. Phillips ◽  
Jeffrey Jestes
Keyword(s):  
Probabilistic Data ◽  
Threshold Monitoring

Effective Clustering Algorithm for Probabilistic Data Stream

2010 ◽  
Vol 20 (5) ◽  
pp. 1313-1328 ◽  
Author(s):  
Dong-Bo DAI ◽  
Gang ZHAO ◽  
Sheng-Li SUN
Keyword(s):  
Data Stream ◽  
Clustering Algorithm ◽  
Probabilistic Data

scholarly journals Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 982 ◽  
Author(s):  
Xin Wu ◽  
Hong Wang ◽  
Guoqian Jiang ◽  
Ping Xie ◽  
Xiaoli Li
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Operating Conditions ◽  
Dynamic Threshold ◽  
Detection Accuracy ◽  
Echo State Network ◽  
Monitoring Method ◽  
Vibration Data ◽  
Static Monitoring ◽  
Threshold Monitoring

Health monitoring of wind turbine gearboxes has gained considerable attention as wind turbines become larger in size and move to more inaccessible locations. To improve the reliability, extend the lifetime of the turbines, and reduce the operation and maintenance cost caused by the gearbox faults, data-driven condition motoring techniques have been widely investigated, where various sensor monitoring data (such as power, temperature, and pressure, etc.) have been modeled and analyzed. However, wind turbines often work in complex and dynamic operating conditions, such as variable speeds and loads, thus the traditional static monitoring method relying on a certain fixed threshold will lead to unsatisfactory monitoring performance, typically high false alarms and missed detections. To address this issue, this paper proposes a reliable monitoring model for wind turbine gearboxes based on echo state network (ESN) modeling and the dynamic threshold scheme, with a focus on supervisory control and data acquisition (SCADA) vibration data. The aim of the proposed approach is to build the turbine normal behavior model only using normal SCADA vibration data, and then to analyze the unseen SCADA vibration data to detect potential faults based on the model residual evaluation and the dynamic threshold setting. To better capture temporal information inherent in monitored sensor data, the echo state network (ESN) is used to model the complex vibration data due to its simple and fast training ability and powerful learning capability. Additionally, a dynamic threshold monitoring scheme with a sliding window technique is designed to determine dynamic control limits to address the issue of the low detection accuracy and poor adaptability caused by the traditional static monitoring methods. The effectiveness of the proposed monitoring method is verified using the collected SCADA vibration data from a wind farm located at Inner Mongolia in China. The results demonstrated that the proposed method can achieve improved detection accuracy and reliability compared with the traditional static threshold monitoring method.

Probabilistic data flow system with two-edge profiling

2000 ◽  
Vol 35 (7) ◽  
pp. 65-72 ◽  
Author(s):  
Eduard Mehofer ◽  
Bernhard Scholz
Keyword(s):  
Data Flow ◽  
Flow System ◽  
Probabilistic Data

scholarly journals A Novel Probabilistic Data Association for Target Tracking in a Cluttered Environment

Sensors ◽  
2016 ◽  
Vol 16 (12) ◽  
pp. 2180 ◽  
Author(s):  
Xiao Chen ◽  
Yaan Li ◽  
Yuxing Li ◽  
Jing Yu ◽  
Xiaohua Li
Keyword(s):  
Target Tracking ◽  
Data Association ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Cluttered Environment

A probabilistic data model and algebra for location-based data warehouses and their implementation

2013 ◽  
Vol 18 (2) ◽  
pp. 357-403 ◽  
Author(s):  
Igor Timko ◽  
Curtis Dyreson ◽  
Torben Bach Pedersen
Keyword(s):  
Data Model ◽  
Data Warehouses ◽  
Probabilistic Data ◽  
And Algebra
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