Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform

International audienceThis article deals with the optimization of a full magnetic indirect drive (FMID) with magnetic gears which uses an analytical model based on subdomain resolution of Laplace's and Poisson's equations of the magnetic gear. A bi-objective analytical optimization is performed with a PSO algorithm with a minimization of the mass of the magnetic parts and a maximization of the gear ratio. This intermediate optimization is a step to optimize a 6MW FMID for a offshore wind turbine with one or two magnetic gears stages. It also compares FMID magnetic parts masses with a direct drive conversion chain with the same generator topology (hybrid excitation synchronous generator)

show abstract

“…However, failures of mechanical gearboxes, induced production interruptions and operating costs are higher than in direct drive design [2]- [3].…”

Section: Nomenclaturementioning

confidence: 99%

Gear ratio optimization of a full magnetic indirect drive chain for wind turbine applications

Desvaux

Multon

Ahmed

et al. 2017

2017 Twelfth International Conference on Ecological Vehicles and Renewable Energies (EVER)

View full text Add to dashboard Cite

show abstract

“…The signal breakdown is performed by a series of convolution operations between the signal and a predefined waveform, called base or mother wavelet, to find their similarity and is expressed as wavelet coefficients. The use of wavelet transforms has been shown to be able to identify bearing failures and has lately been used as a preprocessing tool to machine learning algorithms for automatic detection and classification …”

Section: Introductionmentioning

confidence: 99%

Mother wavelet selection in the discrete wavelet transform for condition monitoring of wind turbine drivetrain bearings

et al. 2019

View full text Add to dashboard Cite

Although the discrete wavelet transform has been used for diagnosing bearing faults for two decades, most work in this field has been done with test rig data. Since field data starts to be made more available, there is a need to shift into application studies. The choice of mother wavelet, ie, the predefined shape used to analyse the signal, has previously been investigated with simulated and test rig data without consensus of optimal choice in literature. Common between these investigations is the use of the wavelet coefficients' Shannon entropy to find which mother wavelet can yield the most useful features for condition monitoring. This study attempts to find the optimal mother wavelet selection using the discrete wavelet transform. Datasets from wind turbine gearbox accelerometers, consisting of enveloped vibration measurements monitoring both healthy and faulty bearings, have been analysed. The bearing fault frequencies' excitation level has been analysed with 130 different mother wavelets, yielding a definitive measure on their performance. Also, the applicability of Shannon entropy as a ranking method of mother wavelets has been investigated. The results show the discrete wavelet transforms ability to identify faults regardless of mother wavelet used, with the excitation level varying no more than 4%. By analysing the Shannon entropy, broad predictions to the excitation level could be drawn within the mother wavelet families but no direct correlation to the main results. Also, the high computational effort of high order Symlet wavelets, without increased performance, makes them unsuitable.

show abstract

“…This work uses the data based 10 approach, as in recent years these techniques seem to have received more attention in academia and industry. For example, Bessa et al [1] propose a new data-driven fault detection and isolation scheme based on time series and data analysis without using any kind of physical modeling; Gong et al [2] propose a mechanical fault detection algorithm by using only nonstationary generator stator current measurements; Teng et al [3] propose a multi-fault detection method using vibration signals originated from a real multi-fault wind turbine gearbox with catastrophic failure. In all the aforementioned papers, the fundamental part is the signal processing method.…”

Section: Introductionmentioning

confidence: 99%

Wind turbine fault detection and classification by means of image texture analysis

Ruiz

Mujica

Alférez

et al. 2018

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

The future of the wind energy industry passes through the use of larger and more flexible wind turbines in remote locations, which are increasingly o↵shore to benefit stronger and more uniform wind conditions. The cost of operation and maintenance of o↵shore wind turbines is approximately 15-35% of the total cost. Of this, 80% goes towards unplanned maintenance issues due to di↵erent faults in the wind turbine components. Thus, an auspicious way to contribute to the increasing demands and challenges is by applying low-cost advanced fault detection schemes. This work proposes a new method for detection and classification of wind turbine actuators and sensors faults in variablespeed wind turbines. For this purpose, time domain signals acquired from the operating wind turbine are represented as two-dimensional matrices to obtain grayscale digital images. Then, the image pattern recognition is processed getting texture features under a multichannel representation. In this work, four types of texture characteristics are used: statistical, wavelet, granulometric and Gabor features. Next, the most significant ones are selected using the conditional mutual criterion. Finally, the faults are detected and distinguished between them (classified) using an automatic classification tool. In particular, a 10-fold cross-validation is used to obtain a more generalized model and evaluates the classification performance. Coupled non-linear aero-hydro-servo-elastic simulations of a 5MW o↵shore type wind turbine are carried out in several fault scenarios. The results show a promising methodology able to detect and classify the most common wind turbine faults.

show abstract

Multi-fault detection and failure analysis of wind turbine gearbox using complex wavelet transform

Cited by 142 publications

References 24 publications

Gear ratio optimization of a full magnetic indirect drive chain for wind turbine applications

Gear ratio optimization of a full magnetic indirect drive chain for wind turbine applications

Mother wavelet selection in the discrete wavelet transform for condition monitoring of wind turbine drivetrain bearings

Wind turbine fault detection and classification by means of image texture analysis

Contact Info

Product

Resources

About