Condition monitoring of wind turbine blades based on self-supervised health representation learning: A conducive technique to effective and reliable utilization of wind energy

Sun, Shilin; Wang, Tianyang; Yang, Hongxing; Chu, Fulei

doi:10.1016/j.apenergy.2022.118882

Cited by 45 publications

(11 citation statements)

References 40 publications

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“…There are three different types of modelling methods: physical methods, traditional statistical models, and artificial intelligence-based models. Physical methods rely on physical equations [6] and use numerical weather prediction (NWP) data and parameters such as ground roughness, topography [7], and elevation [8] to build prediction models. Statistical methods can directly extrapolate the historical wind power series characteristics to obtain the future wind power.…”

Section: Related Workmentioning

confidence: 99%

Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network

Zhang,

Li,

Cheng

et al. 2024

Energies

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High-precision spatial-temporal wind power prediction technology is of great significance for ensuring the safe and stable operation of power grids. The development of artificial intelligence technology provides a new scheme for modeling with strong spatial-temporal correlation. In addition, the existing prediction models are mostly ‘black box’ models, lacking interpretability, which may lead to a lack of trust in the model by power grid dispatchers. Therefore, improving the model to obtain interpretability has become an important challenge. In this paper, an interpretable short-term wind power prediction model based on ensemble deep graph neural network is designed. Firstly, the graph network model (GNN) with an attention mechanism is applied to the aggregate and the spatial-temporal features of wind power data are extracted, and the interpretable ability is obtained. Then, the long short-term memory (LSTM) method is used to process the extracted features and establish a wind power prediction model. Finally, the random sampling algorithm is used to optimize the hyperparameters to improve the learning rate and performance of the model. Through multiple comparative experiments and a case analysis, the results show that the proposed model has a higher prediction accuracy than other traditional models and obtains reasonable interpretability in time and space dimensions.

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Section: Related Workmentioning

confidence: 99%

Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network

Zhang,

Li,

Cheng

et al. 2024

Energies

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“…The generalization of vibration-based SHM methods is therefore needed. In recent years, some researchers have attempted to address this problem by proposing methodologies based on the concept of transfer learning (or domain adaptation) [ 189 , 190 , 191 , 192 ] and self-supervised learning [ 193 ]. It is expected that interest in this topic will keep increasing in future research, especially with the rapid advancements in deep learning research.…”

Section: Challenges and Future Trendsmentioning

confidence: 99%

Unsupervised Learning Methods for Data-Driven Vibration-Based Structural Health Monitoring: A Review

Eltouny

Gomaa

Liang

2023

Sensors

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Structural damage detection using unsupervised learning methods has been a trending topic in the structural health monitoring (SHM) research community during the past decades. In the context of SHM, unsupervised learning methods rely only on data acquired from intact structures for training the statistical models. Consequently, they are often seen as more practical than their supervised counterpart in implementing an early-warning damage detection system in civil structures. In this article, we review publications on data-driven structural health monitoring from the last decade that relies on unsupervised learning methods with a focus on real-world application and practicality. Novelty detection using vibration data is by far the most common approach for unsupervised learning SHM and is, therefore, given more attention in this article. Following a brief introduction, we present the state-of-the-art studies in unsupervised-learning SHM, categorized by the types of used machine-learning methods. We then examine the benchmarks that are commonly used to validate unsupervised-learning SHM methods. We also discuss the main challenges and limitations in the existing literature that make it difficult to translate SHM methods from research to practical applications. Accordingly, we outline the current knowledge gaps and provide recommendations for future directions to assist researchers in developing more reliable SHM methods.

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“…For this, self-supervised learning (SSL) has achieved impressive performance and has found practical application in SHM, especially for efficient damage inspection purposes. For example, Sun et al [76] proposed a self-supervised learning method for conditional monitoring of WT blades to train the sensitive data obtained in volatile operating conditions. The author trained the neural network with only healthy measurements to learn unhealthy conditions using kernel density estimation.…”

Section: Damage Identificationmentioning

confidence: 99%

From data to insight, enhancing structural health monitoring using physics-informed machine learning and advanced data collection methods

Rizvi,

Abbas

2023

Eng. Res. Express

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Owing to recent advancements in sensor technology, data mining, machine learning and cloud computation, structural health monitoring (SHM) based on a data-driven approach has gained more popularity and interest. The data-driven methodology has proved to be more efficient and robust compared with traditional physics-based methods. The past decade has witnessed remarkable progress in machine learning, especially in the field of deep learning which are effective in many tasks and has achieved state-of-the-art results in various engineering domains. In the same manner, deep learning has also revolutionized SHM technology by improving the effectiveness and efficiency of models, as well as enhancing safety and reliability. To some extent, it has also paved the way for implementing SHM in real-world complex civil and mechanical infrastructures. However, despite all the success, deep learning has intrinsic limitations such as its massive-labelled data requirement, inability to generate consistent results and lack of generalizability to out-of-sample scenarios. Conversely, in SHM, the lack of data corresponding to a different state of the structure is still a challenging task. Recent development in physics-informed machine learning methods has provided an opportunity to resolve these challenges in which limited-noisy data and mathematical models are integrated through machine learning algorithms. This method automatically satisfies physical invariants providing better accuracy and improved generalization. This manuscript presents the sate of- -the-art review of prevailing machine learning methods for efficient damage inspection, discuss their limitations, and explains the diverse applications and benefits of physics-informed machine learning in the SHM setting. Moreover, the latest data extraction strategy and the internet of things (IoT) that support the present data-driven methods and SHM are also briefly discussed in the last section.

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Condition monitoring of wind turbine blades based on self-supervised health representation learning: A conducive technique to effective and reliable utilization of wind energy

Cited by 45 publications

References 40 publications

Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network

Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network

Unsupervised Learning Methods for Data-Driven Vibration-Based Structural Health Monitoring: A Review

From data to insight, enhancing structural health monitoring using physics-informed machine learning and advanced data collection methods

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