scholarly journals Optimized Extreme Learning Machine-Based Main Bearing Temperature Monitoring Considering Ambient Conditions’ Effects

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7529
Author(s):  
Zhengnan Hou ◽  
Xiaoxiao Lv ◽  
Shengxian Zhuang
Keyword(s):  
Extreme Learning Machine ◽  
Supervisory Control ◽  
Temperature Monitoring ◽  
False Alarms ◽  
Ambient Conditions ◽  
Main Bearing ◽  
Monitoring Method ◽  
Number Of False Alarms ◽  
Learning Machine ◽  
Harsh Conditions

Wind Turbines (WTs) are exposed to harsh conditions and can experience extreme weather, such as blizzards and cold waves, which can directly affect temperature monitoring. This paper analyzes the effects of ambient conditions on WT monitoring. To reduce these effects, a novel WT monitoring method is also proposed in this paper. Compared with existing methods, the proposed method has two advantages: (1) the changes in ambient conditions are added to the input of the WT model; (2) an Extreme Learning Machine (ELM) optimized by Genetic Algorithm (GA) is applied to construct the WT model. Using Supervisory Control and Data Acquisition (SCADA), compared with the method that does not consider the changes in ambient conditions, the proposed method can reduce the number of false alarms and provide an earlier alarm when a failure does occur.

Sparse representation preserving embedding based on extreme learning machine for process monitoring

2020 ◽  
Vol 42 (10) ◽  
pp. 1895-1907
Author(s):  
Hui Yongyong ◽  
Zhao Xiaoqiang
Keyword(s):  
Sparse Representation ◽  
Extreme Learning Machine ◽  
Process Monitoring ◽  
Nonlinear Dynamic ◽  
Monitoring Method ◽  
Adjacency Graph ◽  
Manifold Structure ◽  
Fast Learning ◽  
Learning Machine ◽  
Adaptive Neighborhood

Extreme learning machine (ELM) is a fast learning mechanism used in many domains. Unsupervised ELM has improved to extract nonlinear features. A nonlinear dynamic process monitoring method named sparse representation preserving embedding based on ELM (SRPE-ELM) is proposed in this paper. First, the noise is removed by sparse representation and the sparse coefficient is applied to construct the adjacency graph. The adjacency graph with a data-adaptive neighborhood can extract dynamic manifold structure better than a specified neighborhood parameter. Secondly, a new objection function considered the sparse reconstruction and output weights is established to extract nonlinear dynamic manifold structure. Thirdly, the statistic SPE and T2 based on SRPE-ELM are built to monitor the whole process. Finally, SRPE-ELM is applied in the IRIS data classification example, a numerical case and Tennessee Eastman benchmark process to verify the effectiveness of process monitoring.

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