Wind turbine gearbox fault prognosis using high-frequency SCADA data

Verma, Ayush; Zappalá, Donatella; Sheng, Shuangwen; Watson, Simon J.

doi:10.1088/1742-6596/2265/3/032067

Cited by 6 publications

(8 citation statements)

References 19 publications

(19 reference statements)

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“…The resulting time series has of course a lower resolution, but the missing values are gone. This method is used in (Verma et al, 2022).…”

Section: Preprocessing Techniquesmentioning

confidence: 99%

“…If it is possible to clean the signal this should be considered, since it will improve the performance of the NBM model. This can be done for example by aggregating the data to a lower resolution (see (Chesterman et al, 2022), (Chesterman et al, 2021) and (Turnbull et al, 2021)), or cleaning or filtering the data using expert knowledge (see (Peter et al, 2022), (Takanashi et al, 2022), (Verma et al, 2022), (Turnbull et al, 2021), (Udo and Yar, 2021), (Beretta M. and J., 2020) and (Kusiak and Li, 2011)).…”

Section: Preprocessing Techniquesmentioning

confidence: 99%

“…Oversampling of the minority class or undersampling of the majority class is an option. A more sophisticated technique is the Synthetic Minority Oversampling Technique (SMOTE) used in for example (Verma et al, 2022). Lastly, in some papers, new features are created by clustering the original signals of the SCADA data in several groups using clustering algorithms.…”

Section: Preprocessing Techniquesmentioning

confidence: 99%

“…The 100 Hz data used in for example (Verma et al, 2022) comes from the Controls Advanced Research Turbine (CART) of the National Renewable Energy Laboratory (NREL). The fact that it is a research turbine makes it possible to sample at much higher rates than what is normally possible.…”

Section: The Data and Signalsmentioning

confidence: 99%

“…Both types can be found in the state-of-the-art literature. Deep neural networks are used in (Black et al, 2022), (Jamil et al, 2022) (transfer learning), (Mazidi et al, 2017), (Verma et al, 2022), (Meyer, 2021) (multi-target neural network), (Turnbull et al, 2021), (Cui et al, 2018), (Sun et al, 2016), (Bangalore and Tjernberg, 2015) (NARX), (Bangalore and Tjernberg, 2014), (Li et al, 2014), (Kusiak and Verma, 2012), (Kusiak and Li, 2011), (Zaher et al, 2009). Another popular type of model is the Autoencoder (AE).…”

Section: Normal Behavior Modeling Algorithmsmentioning

confidence: 99%

See 4 more Smart Citations

Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated ondata from operational wind farms

Chesterman

Verstraeten

Daems

et al. 2023

Preprint

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Abstract. Condition monitoring and failure prediction for wind turbines is currently a hot research topic. This follows from the fact that investments in the wind energy sector have increased dramatically due to the transition to renewable energy production. This paper reviews and implements several techniques from state-of-the-art research on condition monitoring for wind turbines using SCADA data and the Normal Behavior Modelling framework. The first part of the paper consists of an in-depth overview of 5 the current state-of-the-art. In the second part, several techniques from the overview are implemented and compared using data (SCADA and failure data) from five operational wind farms. To this end, 6 demonstration experiments are designed. The first 5 experiments test different techniques for the modeling of the normal behavior. The sixth experiment compares several techniques that can be used for identifying anomalous patterns in the prediction error. The paper concludes with several directions for future work.

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“…The resulting time series has of course a lower resolution, but the missing values are gone. This method is used in (Verma et al, 2022).…”

Section: Preprocessing Techniquesmentioning

confidence: 99%

Section: Preprocessing Techniquesmentioning

confidence: 99%

Section: Preprocessing Techniquesmentioning

confidence: 99%

Section: The Data and Signalsmentioning

confidence: 99%

Section: Normal Behavior Modeling Algorithmsmentioning

confidence: 99%

See 3 more Smart Citations

Overview of normal behavior modeling approaches for SCADA-based wind turbine condition monitoring demonstrated ondata from operational wind farms

Chesterman

Verstraeten

Daems

et al. 2023

Preprint

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Convolutional neural network framework for wind turbine electromechanical fault detection

et al. 2023

Self Cite

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Effective and timely health monitoring of wind turbine gearboxes and generators is essential to reduce the costs of operations and maintenance activities, especially offshore. This paper presents a scalable and lightweight convolutional neural network (CNN) framework using high‐dimensional raw condition monitoring data for the automatic detection of multiple wind turbine electromechanical faults. The proposed approach leverages the potential of combining information from a variety of signals to learn features and to discriminate the types of fault and their severity. As a result of the CNN layers used to extract features from the signals, this architecture works in the time domain and can digest high‐resolution multi‐sensor data streams in real‐time. To overcome the inherent black‐box nature of AI models, this research proposes two interpretability techniques, multidimensional scaling and layer‐wise relevance propagation, to analyse the proposed model's inner‐working and identify the signal features relevant for fault classification. Experimental results show high performance and classification accuracies above 99.9% for all fault cases tested, demonstrating the efficacy of the proposed fault‐detection system.

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