Comparison of condition monitoring techniques in assessing fault severity for a wind turbine gearbox under non-stationary loading

Inturi, Vamsi; Sabareesh, G. R.; Penumakala, Pavan Kumar

doi:10.1016/j.ymssp.2019.01.038

Cited by 81 publications

(44 citation statements)

References 23 publications

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“…It decomposes the signal to high frequency detail and low frequency approximation coefficients and the approximation coefficients are subjected to next level of decomposition. Here, four-level decomposition with the help of Haar wavelet is performed to achieve the fourth level approximation coefficients corresponding to the frequency band of 0-625 Hz and statistical features are computed from for vibration and acoustic signals individually [4]. A total of thirteen statistical features from vibration signals, thirteen from acoustic signals and the overall oil particle deposition rate are computed.…”

Section: Methodsmentioning

confidence: 99%

“…All the mating components were lubricated by gear oil (80w-90), refer Fig. 1 [4]. The experiments pertaining to gears were performed on pinion and the experiments related to bearings were performed on test bearing of each speed stage.…”

Section: Experimental Test Rigmentioning

confidence: 99%

“…CM involves the installation of sensors, data acquisition, processing the acquired signal, evoking the diagnostic information and decision making with the help of software driven algorithms [1]. Vibration and lubrication oil monitoring are the widely implemented CM strategies in order to monitor the condition of wind turbine gearbox [2][3][4]. However, contemporary investigations are more concerned about the integration of individual CM strategies as it is capable of diagnosing various types of defects, quantifying the defect severity and suitable for various ranges of operating speeds [5].…”

Section: Introductionmentioning

confidence: 99%

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Hyperparameter optimization for enabling multi-level feature classification in a wind turbine gearbox

Inturi¹,

Chetti²,

N³

et al. 2019

Vib. proced.

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Majority of the previous research investigations on fault diagnostics in a wind turbine gearbox are limited to binary classification, i.e., either detecting the type of defect or severities of defect. However, wind turbine gearbox consists of multiple speed stages and components, therefore performing the binary classification is not adequate. In the present study, a multi-level classification scheme which is capable of classifying the defects by stage, component, type of defect and severity level is proposed. Experiments are performed and the response is recorded through vibration, acoustic signal and lubrication oil analysis. Later, an integrated multi-variable feature set is achieved by combining the statistical features of the above mentioned individual condition monitoring strategies. Further, the obtained integrated multi-variable feature set is subjected to multi-level classification using various machine learning models and the learning model that best suits for carrying the multi-level classification is investigated. Finally, the hyperparameters of the learning models are optimized by an iterative process of reducing the objective function. It is observed that, optimized support vector machine model has yielded favorable results when compared to other machine learning models with the overall classification accuracy of 82.52 % for the four-level classification.

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Section: Methodsmentioning

confidence: 99%

Section: Experimental Test Rigmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hyperparameter optimization for enabling multi-level feature classification in a wind turbine gearbox

Inturi¹,

Chetti²,

N³

et al. 2019

Vib. proced.

View full text Add to dashboard Cite

show abstract

“…The stability of the load and the output power has become significant issues in large-scale offshore wind turbines. There are many cases of fatigue failure of bearings and gearboxes prior to the end of the design life, which indicates the necessity of load mitigation control [3]. As the blade inertia of large-scale offshore machines is very large, traditional pitch control methods are unable to handle the fast-changing aerodynamic load fluctuations [4].…”

Section: Introductionmentioning

confidence: 99%

Performance and Effect of Load Mitigation of a Trailing-Edge Flap in a Large-Scale Offshore Wind Turbine

Cai

Wang

et al. 2020

JMSE

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As a result of the large-scale trend of offshore wind turbines, wind shear and turbulent wind conditions cause significant fluctuations of the wind turbine’s torque and thrust, which significantly affect the service life of the wind turbine gearbox and the power output stability. The use of a trailing-edge flap is proposed as a supplement to the pitch control to mitigate the load fluctuations of large-scale offshore wind turbines. A wind turbine rotor model with a trailing-edge flap is established by using the free vortex wake (FVW) model. The effects of the deflection angle of the trailing-edge flap on the load distribution of the blades and wake flow field of the offshore wind turbine are analyzed. The wind turbine load response under the control of the trailing-edge flap is obtained by simulating shear wind and turbulent wind conditions. The results show that a better control effect can be achieved in the high wind speed condition because the average angle of attack of the blade profile is small. The trailing-edge flap significantly changes the load distribution of the blade and the wake field and mitigates the low-frequency torque and thrust fluctuations of the turbine rotor under the action of wind shear and turbulent wind.

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“…Fault diagnosis is defined as detection of physical fault in a mechanical system and classification of the fault type whereas fault prognosis is concerned about predicting the remaining useful life (RUL) of the mechanical system [2]. Previous researchers have used vibration analysis in order to diagnose the defect present in the wind turbine gearbox and further quantified the severity level using various machine learning algorithms [3,4]. As the acquired vibration signatures are noisy and have non-linear nature, recent studies are more concerned about the implementation of various signal processing algorithms such as wavelet transform, empirical mode decomposition to extract the fault sensitive information from the acquired data [3,5].…”

Section: Introductionmentioning

confidence: 99%

PCA based health indicator for remaining useful life prediction of wind turbine gearbox

Praveen¹,

Shah²,

Pandey³

et al. 2019

Vib. proced.

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Fault prognosis of wind turbine gearbox has received considerable attention as it predicts the remaining useful life which further allows the scheduling of maintenance strategies. However, the studies related towards the RUL prediction of wind turbine gearbox are limited, because of the complexity of gearbox, acute changes in the operating conditions and non-linear nature of the acquired vibration signals. In this study, a health indicator is constructed in order to predict the remaining useful life of the wind turbine gearbox. Run to fail experiments are performed on a laboratory scaled wind turbine gearbox of overall gear ratio 1:100. Vibration signals are acquired and decomposed through continuous wavelet transform to obtain the wavelet coefficients. Various statistical features are computed from the wavelet coefficients which return form high-dimensional input feature set. Principal component analysis is performed to reduce the dimensionality and principal components (PCs) are computed from the input feature set. PC1 is considered as the health indicator and subjected to further smoothening by linear rectification technique. Exponential degradation model is fit to the considered health indicator and the model is able to predict the RUL of the gearbox with an error percentage of 2.73 %.

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Comparison of condition monitoring techniques in assessing fault severity for a wind turbine gearbox under non-stationary loading

Cited by 81 publications

References 23 publications

Hyperparameter optimization for enabling multi-level feature classification in a wind turbine gearbox

Hyperparameter optimization for enabling multi-level feature classification in a wind turbine gearbox

Performance and Effect of Load Mitigation of a Trailing-Edge Flap in a Large-Scale Offshore Wind Turbine

PCA based health indicator for remaining useful life prediction of wind turbine gearbox

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