A data-driven approach for fault diagnosis in gearbox of wind energy conversion system

Krüger, Minjia; Ding, Steven X.; Haghani, Adel; Engel, Peter; Jeinsch, Torsten

doi:10.1109/systol.2013.6693901

Cited by 9 publications

(8 citation statements)

References 14 publications

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“…The threshold is determined according to Equation (16). In order to verify the fault detection performance, the existing PCA and dynamic PCA (DPCA) based wind turbine fault detection methods [25,27] are employed. In terms of the reconstruction error, PCA and DPCA use the squared prediction error (SPE) statistic (Q statistic) for residual evaluation and threshold calculation.…”

Section: Results and Analysismentioning

confidence: 99%

“…As a representative classification method, the FDA determines a set of projection vectors that minimize the scatter within each class while maximizing the scatter between the classes [24]. Krüger et al adopted PCA to investigate the correlations of the measured process data for the detection of process abnormalities that happened in wind turbine gearbox and then used the FDA to classify the fault types [25]. Pozo et al used PCA and statistical hypothesis testing to develop a fault detection scheme for wind turbine [26].…”

Section: Introductionmentioning

confidence: 99%

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Floating offshore wind turbine fault diagnosis via regularized dynamic canonical correlation and fisher discriminant analysis

Liu

Ferrari

et al. 2021

IET Renewable Power Gen

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Over the past decades, Floating Offshore Wind Turbine (FOWT) has gained increasing attention in wind engineering due to the rapidly growing energy demands. However, difficulties in turbine maintenance will increase due to the harsh operational conditions. Fault diagnosis techniques play a crucial role to enhance the reliability of FOWTs and reduce the cost of offshore wind energy. In this paper, a novel data‐driven fault diagnosis method using regularized dynamic canonical correlation analysis (RDCCA) and Fisher discriminant analysis (FDA) is proposed for FOWTs. Specifically, to overcome the collinearity problem that exists in measured process data, dynamic canonical correlation analysis with a regularization scheme, is developed to exploit the relationship between input and output signals. Then, the residual signals are generated from the established RDCCA model for fault detection. To further classify the fault type, an FDA model is trained from the residual signals of different training faulty data sets. Simulations on a FOWT baseline model based on the widely used National Renewable Energy Laboratory FAST simulator are carried out to demonstrate the feasibility and efficacy of the proposed fault detection and classification method. Results have shown many salient features of the proposed method with potential applications in FOWTs.

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Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Floating offshore wind turbine fault diagnosis via regularized dynamic canonical correlation and fisher discriminant analysis

Liu

Ferrari

et al. 2021

IET Renewable Power Gen

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“…Briefly, both of them are introduced here. uses the time-lagged version of the system input and output to develop a model for monitoring purposes, and has been widely used for the fault detection of dynamic systems [46,48]. Rato and Reis [49] established the DPCA model based on decorrelated residuals to detect several faults and illustrated the reliability of such an approach in the fault detection.…”

Section: Comparison To Other Approachesmentioning

confidence: 99%

Fault Diagnosis of the 10MW Floating Offshore Wind Turbine Benchmark: a Mixed Model and Signal-based Approach

Liu

Ferrari

et al. 2020

Preprint

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Floating Offshore Wind Turbines (FOWTs) operate in the harsh marine environment with limited accessibility and maintainability. Not only failures are more likely to occur than in land-based turbines, but also corrective maintenance is more expensive. In the present study, a mixed model and signal-based Fault Diagnosis (FD) architecture is developed to detect and isolate critical faults in FOWTs. More specifically, a model-based scheme is developed to detect and isolate the faults associated with the turbine system. It is based on a fault detection and approximation estimator and fault isolation estimators, with time-varying adaptive thresholds to guarantee against false-alarms. In addition, a signal-based scheme is established, within the proposed architecture, for detecting and isolating two representative mooring lines faults. For the purpose of verification, a 10MW FOWT benchmark is developed and its operating conditions, which contains predefined faults, are simulated by extending the high-fidelity simulator. Based on it, the effectiveness of the proposed architecture is illustrated. In addition, the advantages and limitations are discussed by comparing its fault detection to the results delivered by other approaches. Results show that the proposed architecture has the best performance in detecting and isolating the critical faults in FOWTs under diverse operating conditions.

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“…are available for the investigation of the current WEC condition [Hameed et al, 2009]. The Supervisory Control and Data Acquisition (SCADA) system collects online all kinds of measurement data from the WEC and record their operation status, which makes it possible to realize the WEC condition monitoring with data-driven multivariate methods (such as artificial intelligence [Kusiak et al, 2012], support vector machine [Laouti et al, 2011] and PCA method [Krueger et al, 2013]). However, as addressed in [Huang, 2008], uncertainties in the practice is unavoidable.…”

Section: Introductionmentioning

confidence: 99%

“…Fischer et al [2007] has studied the functional failure modes of WEC major components, identified the root causes and suggested possible measures to prevent the failure causes, which forms a basis for the development of an optimized RCM strategy. Nevertheless, it has been pointed out in [Krueger et al, 2013], although the prior knowledge of the abnormality is available, it is still difficult to achieve a correct fault identification since the operating data of different WECs might be slightly influenced by their different operating conditions.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Maintenance Support System for Wind Energy Conversion Systems

Krueger

Haghani

Ding

et al. 2014

IFAC Proceedings Volumes

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A data-driven approach for fault diagnosis in gearbox of wind energy conversion system

Cited by 9 publications

References 14 publications

Floating offshore wind turbine fault diagnosis via regularized dynamic canonical correlation and fisher discriminant analysis

Floating offshore wind turbine fault diagnosis via regularized dynamic canonical correlation and fisher discriminant analysis

Fault Diagnosis of the 10MW Floating Offshore Wind Turbine Benchmark: a Mixed Model and Signal-based Approach

A Data-Driven Maintenance Support System for Wind Energy Conversion Systems

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