Research on Fault Classification of Wind Turbine Based on IMF Kurtosis and PSO-SOM-LVQ

Shi, Xianjiang; Li, Wantao; Gao, Qingkang; Guo, Hua

doi:10.1109/itnec.2017.8284935

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…To identify wind turbine bearing issues, Ref. [30] provide a feature selection and learning vector quantization (LVQ) neural network technique combination. The right features are extracted using Empirical Mode Decomposition (EMD).…”

Section: Bearing Failurementioning

confidence: 99%

A Review of Predictive Techniques Used to Support Decision Making for Maintenance Operations of Wind Turbines

2023

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The analysis of reliable studies helps to identify the credibility, scope, and limitations of various techniques for condition monitoring of a wind turbine (WT) system’s design and development to reduce the operation and maintenance (O&M) costs of the WT. In this study, recent advancements in data-driven models for condition monitoring and predictive maintenance of wind turbines’ critical components (e.g., bearing, gearbox, generator, blade pitch) are reviewed. We categorize these models according to data-driven procedures, such as data descriptions, data pre-processing, feature extraction and selection, model selection (classification, regression), validation, and decision making. Our findings after reviewing extensive relevant articles suggest that (a) SCADA (supervisory control and data acquisition) data are widely used as they are available at low cost and are extremely practical (due to the 10 min averaging time), but their use is in some sense nonspecific. (b) Unstructured data and pre-processing remain a significant challenge and consume a significant time of whole machine learning model development. (c) The trade-off between the complexity of the vibration analysis and the applicability of the results deserves further development, especially with regards to drivetrain faults. (d) Most of the proposed techniques focus on gearbox and bearings, and there is a need to apply these models to other wind turbine components. We explain these findings in detail and conclude with a discussion of the main areas for future work in this domain.

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Section: Bearing Failurementioning

confidence: 99%

A Review of Predictive Techniques Used to Support Decision Making for Maintenance Operations of Wind Turbines

2023

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“…Data-driven methods, also known as model-free models, only need historical system data to construct fault diagnosis systems (Jing et al, 2017). In Shi et al (2017), real-time data is used to control the simulation process to achieve good fault detection performance. Environmental variations impact the fault detection capability of CMS; hence a controlled simulation framework is suggested.…”

Section: Advanced Data-driven Technologies For Wind Turbinesmentioning

confidence: 99%

Section: Advanced Data-driven Technologies For Wind Turbinesmentioning

confidence: 99%

“…Parametric models have a finite set of parameters in a parametric vector. At the same time, non-parametric models are defined by parametric vectors which are unbound in length (Shi et al, 2017). Bogoevska et al (2017) proposed an interesting overview of the bi-component condition/performance monitoring strategy a for wind turbine that incorporates both environmental and vibrational datasets as shown in Figure 3.…”

Section: Regression Modelsmentioning

confidence: 99%

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SCADA data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trends

et al. 2022

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This paper reviews the recent advancement made in data-driven technologies based on SCADA data for improving wind turbines’ operation and maintenance activities (e.g. condition monitoring, decision support, critical components failure detections) and the challenges associated with them. Machine learning techniques applied to wind turbines’ operation and maintenance (O&M) are reviewed. The data sources, feature engineering and model selection (classification, regression) and validation are all used to categorise these data-driven models. Our findings suggest that (a) most models use 10-minute mean SCADA data, though the use of high-resolution data has shown greater advantages as compared to 10-minute mean value but comes with high computational challenges. (b) Most of SCADA data are confidential and not available in the public domain which slows down technological advancements. (c) These datasets are used for both, the classification and regression of wind turbines but are used in classification extensively. And, (d) most commonly used data-driven models are neural networks, support vector machines, probabilistic models and decision trees and each of these models has its own merits and demerits. We conclude the paper by discussing the potential areas where SCADA data-based data-driven methodologies could be used in future wind energy research.

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Failure Diagnosis of Wind Turbine Bearing Using Feature Extraction and a Neuro-Fuzzy Inference System (ANFIS)

Kordestani

Rezamand

Carriveau

et al. 2019

Advances in Computational Intelligence

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Research on Fault Classification of Wind Turbine Based on IMF Kurtosis and PSO-SOM-LVQ

Cited by 4 publications

References 6 publications

A Review of Predictive Techniques Used to Support Decision Making for Maintenance Operations of Wind Turbines

A Review of Predictive Techniques Used to Support Decision Making for Maintenance Operations of Wind Turbines

SCADA data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trends

Failure Diagnosis of Wind Turbine Bearing Using Feature Extraction and a Neuro-Fuzzy Inference System (ANFIS)

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