scholarly journals Electrostatic Sensor Application for On-Line Monitoring of Wind Turbine Gearboxes

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3574 ◽  
Author(s):  
Huijie Mao ◽  
Hongfu Zuo ◽  
Han Wang
Keyword(s):  
Wind Turbine ◽  
Wear Debris ◽  
Bench Test ◽  
Recognition Method ◽  
Wind Turbine Gearbox ◽  
Practical Applications ◽  
Electrostatic Induction ◽  
Oil Flow ◽  
On Line ◽  
Application Potential

The oil-line electrostatic sensor (OLES) is a new online monitoring technology for wear debris based on the principle of electrostatic induction that has achieved good measurement results under laboratory conditions. However, for practical applications, the utility of the sensor is still unclear. The aim of this work was to investigate in detail the application potential of the electrostatic sensor for wind turbine gearboxes. Firstly, a wear debris recognition method based on the electrostatic sensor with two-probes is proposed. Further, with the wind turbine gearbox bench test, the performance of the electrostatic sensor and the effectiveness of the debris recognition method are comprehensively evaluated. The test demonstrates that the electrostatic sensor is capable of monitoring the debris and indicating the abnormality of the gearbox effectively using the proposed method. Moreover, the test also reveals that the background signal of the electrostatic sensor is related to the oil temperature and oil flow rate, but has no relationship to the working conditions of the gearbox. This research brings the electrostatic sensor closer to practical applications.

scholarly journals Study on online monitoring wear debris sensor of wind turbine gearbox

2019 ◽  
Vol 2019 (23) ◽  
pp. 9092-9096
Author(s):  
Xiangqi Zhang ◽  
Xiaoli Xu ◽  
Yunbo Zuo ◽  
Yuhai Gu
Keyword(s):  
Wind Turbine ◽  
Wear Debris ◽  
Online Monitoring ◽  
Wind Turbine Gearbox

Research on Online Monitoring of Gear Fault of Wind Power Gearbox Based on Undecimated Wavelet Transformation

2011 ◽  
Vol 383-390 ◽  
pp. 4928-4931 ◽  
Author(s):  
Fu Cheng Zhou
Keyword(s):  
Wind Turbine ◽  
Wavelet Transformation ◽  
Online Monitoring ◽  
Vibration Signal ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transformation ◽  
Wind Turbine Gearbox ◽  
Gear Fault ◽  
On Line ◽  
Undecimated Wavelet

According to fault characteristics of wind turbine gearbox, an On-line Monitoring System of wind turbine gearbox based on the undecimated discrete wavelet transformation was developed. Measured the vibration signal of gear fault in the laboratory, The signal after undecimated wavelet transformation can be more accurate and clear display characteristics, which was conducive to diagnose gear failure quickly and effectively.

Heuristic Algorithms for Waste Load Allocation in a River Basin

1989 ◽  
Vol 21 (8-9) ◽  
pp. 1057-1064 ◽  
Author(s):  
Vijay Joshi ◽  
Prasad Modak
Keyword(s):  
Dynamic Programming ◽  
Heuristic Algorithms ◽  
Water Quality Management ◽  
User Preferences ◽  
Theory And Practice ◽  
Waste Load Allocation ◽  
Practical Applications ◽  
Waste Load ◽  
On Line ◽  
The One

Waste load allocation for rivers has been a topic of growing interest. Dynamic programming based algorithms are particularly attractive in this context and are widely reported in the literature. Codes developed for dynamic programming are however complex, require substantial computer resources and importantly do not allow interactions of the user. Further, there is always resistance to utilizing mathematical programming based algorithms for practical applications. There has been therefore always a gap between theory and practice in systems analysis in water quality management. This paper presents various heuristic algorithms to bridge this gap with supporting comparisons with dynamic programming based algorithms. These heuristics make a good use of the insight gained in the system's behaviour through experience, a process akin to the one adopted by field personnel and therefore can readily be understood by a user familiar with the system. Also they allow user preferences in decision making via on-line interaction. Experience has shown that these heuristics are indeed well founded and compare very favourably with the sophisticated dynamic programming algorithms. Two examples have been included which demonstrate such a success of the heuristic algorithms.

Fault Diagnosis of Wind Turbine Gearbox Based on Improved QPSO Algorithm

2019 ◽  
Vol 12 (3) ◽  
pp. 277-283
Author(s):  
Jiatang Cheng ◽  
Yan Xiong
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Bp Neural Network ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Wind Turbine Gearbox ◽  
Bp Network ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

Background: The effective diagnosis of wind turbine gearbox fault is an important means to ensure the normal and stable operation and avoid unexpected accidents. Methods: To accurately identify the fault modes of the wind turbine gearbox, an intelligent diagnosis technology based on BP neural network trained by the Improved Quantum Particle Swarm Optimization Algorithm (IQPSOBP) is proposed. In IQPSO approach, the random adjustment scheme of contractionexpansion coefficient and the restarting strategy are employed, and the performance evaluation is executed on a set of benchmark test functions. Subsequently, the fault diagnosis model of the wind turbine gearbox is built by using IQPSO algorithm and BP neural network. Results: According to the evaluation results, IQPSO is superior to PSO and QPSO algorithms. Also, compared with BP network, BP network trained by Particle Swarm Optimization (PSOBP) and BP network trained by Quantum Particle Swarm Optimization (QPSOBP), IQPSOBP has the highest diagnostic accuracy. Conclusion: The presented method provides a new reference for the fault diagnosis of wind turbine gearbox.

scholarly journals Research on the application of on-line monitoring technology for bolt axial force of wind turbine

2021 ◽  
Vol 1043 (3) ◽  
pp. 032052
Author(s):  
Linlin Cao ◽  
Kai Liu ◽  
Zhongwei Zhang ◽  
Wendong Sun ◽  
Weiliang Chen
Keyword(s):  
Wind Turbine ◽  
Axial Force ◽  
Monitoring Technology ◽  
On Line
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