scholarly journals The Application of Heterogeneous Information Fusion in Misalignment Fault Diagnosis of Wind Turbines

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1655 ◽  
Author(s):  
Yancai Xiao ◽  
Yujia Wang ◽  
Zhengtao Ding
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Wind Turbines ◽  
Heterogeneous Information ◽  
Misalignment Fault

Multi Heterogeneous Information Fusion to Facilitate Fault Diagnosis of Mechatronic Equipments

2011 ◽  
Vol 143-144 ◽  
pp. 703-706
Author(s):  
Li Jun Song ◽  
Zheng Hu
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Information Fusion ◽  
Synthesis Process ◽  
Fusion Method ◽  
Test Results ◽  
Heterogeneous Information ◽  
Local Decision ◽  
Usage Data

This paper describes a strategy for fault diagnosis of mechatronic equipments. The basic idea of information fusion is not only to take into account on-site sensor measurements, but also other factors that could potentially illustrate the essential course of an equipment's evolving faults. For example, these distributed factors might include components information, overhaul test results, usage data, expert experience, and others. Integrating the above produces a more refined diagnosis analysis compared to a local decision based on on-site information alone. However, measurements and other evidential contents were originally meant to serve different goals, i.e. the multi sources might be highly heterogeneous. To allow a unified representation we propose in this paper a formalization modeling based on ontology. Finally, we demonstrate the diagnostic synthesis process in terms of a certain information fusion method, i.e. Bayesian Network.

Fault Classification of Hydroelectric Generating Unit Based on Improved Evidence Theory

2014 ◽  
Vol 7 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Jiatang Cheng ◽  
Li Ai ◽  
Zhimei Duan ◽  
Yan Xiong
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
Fault Classification ◽  
Fusion Method ◽  
Inconsistent Result ◽  
High Conflict

Aiming at the problem of the conventional vibration fault diagnosis technology with inconsistent result of a hydroelectric generating unit, an information fusion method was proposed based on the improved evidence theory. In this algorithm, the original evidence was amended by the credibility factor, and then the synthesis rule of standard evidence theory was utilized to carry out information fusion. The results show that the proposed method can obtain any definitive conclusion even if there is high conflict evidence in the synthesis evidence process, and may avoid the divergent phenomenon when the consistent evidence is fused, and is suitable for the fault classification of hydroelectric generating unit.

scholarly journals Fault Diagnosis of Bearings Based on Multi-Sensor Information Fusion and 2D Convolutional Neural Network

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 23717-23725
Author(s):  
Jiaxing Wang ◽  
Dazhi Wang ◽  
Sihan Wang ◽  
Wenhui Li ◽  
Keling Song
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Information Fusion ◽  
Sensor Information

scholarly journals Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 975
Author(s):  
Yancai Xiao ◽  
Jinyu Xue ◽  
Mengdi Li ◽  
Wei Yang
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Gear Tooth ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Empirical Wavelet Transform ◽  
Low Pass

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.

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