Improved condition monitoring technique for wind turbine gearbox and shaft stress detection

Salem, A.; Abu-Siada, A.; Islam, Syed

doi:10.1049/iet-smt.2016.0338

Cited by 12 publications

(7 citation statements)

References 31 publications

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“…Qian Peng presented a single hidden-layer feed forward neural network (SLFN), trained using an extreme learning machine (ELM) algorithm, for condition monitoring of wind turbines [57]. Salem introduced an improved technique to monitor the condition of the wind turbine gearbox based on gearbox vibration and shaft torque signatures analyses [58]. With the development of condition monitoring technology, the data collected by sensors are voluminous and much faster than before, Peng Qian proposed a novel wind turbine condition monitoring method based on cloud computing.…”

Section: The Cms Performance Methodsmentioning

confidence: 99%

A review on wind turbines gearbox fault diagnosis methods

Gu¹,

Liu²,

Gao³

et al. 2021

J. vibroeng.

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As an renewable and clean energy of the world, wind energy has gained more and more attention and its fault diagnosis becomes more and more important. The gearbox, as the kernel component of the wind turbine system, it's robust conditions have a great influence on the whole wind turbines system. Wind turbine gearbox has complex structure, which is usually composed of solar planetary gearbox and cylindrical gearbox. In the process of operation, various kinds of faults easily occur, resulting in serious losses. Once the wind turbine gearbox is not functioning as smoothly as it could be, it may result in large economic losses for the company and owner. At the same time, the failure rate of wind turbine gearbox has always been high because of complicated mechanic structure and special motion. Therefore, the tasks of reducing the downtime and increasing the productivity of wind turbine gearbox are urgent. This paper reviewed some research results of faults diagnosis on wind turbines gearbox, such as time-frequency analysis method, vibration based methods, nondestructive testing methods, etc. Meanwhile, this paper finds out some key problems and the channel of the resolution of the issue in order to supply some information for the further research of wind turbines gearbox.

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Section: The Cms Performance Methodsmentioning

confidence: 99%

A review on wind turbines gearbox fault diagnosis methods

Gu¹,

Liu²,

Gao³

et al. 2021

J. vibroeng.

View full text Add to dashboard Cite

show abstract

“…Gears as the most important transmission components, and higher and higher transmission efficiency and smoothness are required in the manufacturing industry. Wind power generation system has higher and higher requirements for the motion stability of gearbox, and the important index of stability evaluation for gear transmission is the gear comprehensive deviation [1][2][3]. Compared with single deviation measurement instrument, gear comprehensive deviation measurement efficiency is higher, especially gear single-sided meshing measurement is more close to the using status of gears, so it is widely used in rapid gears measurement field.…”

Section: Introductionmentioning

confidence: 99%

Research on an Improved Sliding Extracting Algorithm of Composite Deviation for Wind Turbine Gear

Zhang

Haofang²,

Gao³

2022

J. Phys.: Conf. Ser.

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One tooth radial composite deviation and one tooth tangential composite deviation are important parameters of wind turbine gears whose operation life cycle can be influenced. An improved sliding extracting algorithm of composite deviation is proposed. It just only calculates those parts that need to be recalculated, avoids a large number of invalid searching calculations, and greatly improves the efficiency. The experimental results show that the improved sliding extracting algorithm of composite deviation has the same results as the traditional extracting algorithm, and the efficiency of the extracting algorithm is improved nearly 100 times. The improved sliding extracting algorithm of total composite deviation is proposed in this paper, and theoretically the algorithm efficiency is one time than traditional algorithm. The algorithms proposed in this paper can improve the efficiency of radial composite deviation and tangential composite deviation, and can be applied to gear singleflank rolling tester and gear double-flank rolling tester, especially in wind turbine gear field.

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“…Renewable energy sources, especially wind energy is presently the most popular technology as there were more than 282.48 GW installed capacity at the end of 2012 [1][2][3][4]. There is a need for an early fault detection in this increasingly popular technology, since the early fault detection in WT can help to reduce the cost for effective maintenance and operation [1,[5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Fault detection and classification in wind turbine by using artificial neural network

Fadzail¹,

Zali²

2019

IJPEDS

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Wind turbine is one of the present renewable energy sources that has become the most popular. The operational and maintenance cost is continuously increasing, especially for wind generator. Early fault detection is very important to optimise the operational and maintenance cost. The goal of this project is to study fault detection and classification for a wind turbine (WT) by using artificial neural network (ANN). In this project, a single phase fault was placed at 9 MW doubly-fed induction generator (DFIG) WT in MATLAB Simulink. The WT was tested under different conditions, i.e., normal condition, fault at Phase A, Phase B and Phase C. The simulation results were used as inputs in the ANN model for training. Then, a new set of data was taken under different conditions as inputs for ANN fault classifier. The target outputs of ANN fault classifier were set as ‘0’ or ‘1’, based on the fault condition. Results obtained showed that the ANN fault classifier outputs had followed the target outputs. In conclusion, the WT fault detection and classification method by using ANN were successfully developed.

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Improved condition monitoring technique for wind turbine gearbox and shaft stress detection

Cited by 12 publications

References 31 publications

A review on wind turbines gearbox fault diagnosis methods

A review on wind turbines gearbox fault diagnosis methods

Research on an Improved Sliding Extracting Algorithm of Composite Deviation for Wind Turbine Gear

Fault detection and classification in wind turbine by using artificial neural network

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