Application of multisensor data fusion based on RBF neural networks for fault diagnosis of SAMS

2004 ◽  
Author(s):  
Chunling Fan ◽  
Zhihua Jin ◽  
Jing Zhang ◽  
Weifeng Tian
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Data Fusion ◽  
Rbf Neural Networks ◽  
Multisensor Data Fusion ◽  
Multisensor Data

Multisensor Data Fusion Using Neural Networks

2006 ◽  
Author(s):  
N. Yadaiah ◽  
L. Singh ◽  
R.S. Bapi ◽  
V.S. Rao ◽  
B.L. Deekshatulu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Data Fusion ◽  
Multisensor Data Fusion ◽  
Multisensor Data

scholarly journals A CBA-KELM-Based Recognition Method for Fault Diagnosis of Wind Turbines with Time-Domain Analysis and Multisensor Data Fusion

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Xiafei Long ◽  
Ping Yang ◽  
Hongxia Guo ◽  
Zhuoli Zhao ◽  
Xiwen Wu
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Wind Turbines ◽  
Time Domain ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Wind Turbine Gearbox ◽  
Operational Conditions ◽  
Multisensor Data Fusion ◽  
Multisensor Data

Fault diagnosis technology (FDT) is an effective tool to ensure stability and reliable operation in wind turbines. In this paper, a novel fault diagnosis methodology based on a cloud bat algorithm (CBA)-kernel extreme learning machines (KELM) approach for wind turbines is proposed via combination of the multisensor data fusion technique and time-domain analysis. First, the derived method calculates the time-domain indices of raw signals, and the fused time-domain indexes dataset are obtained by the multisensor data fusion. Then, the CBA-based KELM recognition model that can identify fault patterns of a wind turbine gearbox (WTB) is automatically established with the fused dataset. The dataset includes a large number of samples involving 6 fault types under different operational conditions by 5 accelerometers. The effectiveness and feasibility of this proposed method are proved by adopting the datasets originated from the test rig, and it achieves a diagnostic accuracy of 96.25%. Finally, compared with the other peer-to-peer methods, the experimental classification results show that the proposed CBA-KELM technique has the best performances.

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