Crack Detection of Fan Blade Based on Natural Frequencies

Yu, Mei; Fu, Sheng; Gao, Yinbo; Zheng, Hao; Xu, Yonggang

doi:10.1155/2018/2095385

Cited by 14 publications

(4 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Joshuva et al [22] applied machine learning to identify the impact of various blade failure circumstances on unit blades. Yu et al [23] applied computational fluid dynamics and finite element analysis to numerically simulate the damage detection of single cracked blades and proposed an effective method to predict crack location and size based on the variation the natural frequency of wind turbine blades. Zhang et al [24] proposed a method for detecting blade faults by fusing tip clearance information with BTT data, which was validated by 16 sets of experiments.…”

Section: Introductionmentioning

confidence: 99%

Blade Crack Diagnosis Based on Blade Tip Timing and Convolution Neural Networks

et al. 2023

View full text Add to dashboard Cite

The diagnosis of blade crack faults is critical to ensuring the safety of turbomachinery. Blade tip timing (BTT) is a non-contact vibration displacement measurement technique, which has been extensively studied for blade vibration condition monitoring recently. The fault diagnosis methods based on deep learning can be summarized as studying the internal logical relationship of data, automatically mining features, and intelligently identifying faults. This research proposes a crack fault diagnostic method based on BTT measurement data and convolutional neural networks (CNNs) for the crack fault detection of blades. There are two main aspects: the numerical analysis of the rotating blade crack fault diagnosis and the experimental research in rotating blade crack fault diagnosis. The results show that the method outperforms many other traditional machine learning models in both numerical models and tests for diagnosing the depth and location of blade cracks. The findings of this study contribute to the real-time online crack fault diagnosis of blades.

show abstract

Section: Introductionmentioning

confidence: 99%

Blade Crack Diagnosis Based on Blade Tip Timing and Convolution Neural Networks

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The traditional fault detection methods on wind turbine blades include acoustic emission detection [11], infrared imaging detection [12], and abnormal vibration detection [13]. Although these traditional fault detection methods have the advantages of fast detection speed and strong real-time, however, they are usually unable to detect the faults of the wind turbine blades in the working state, and have extremely high requirements on the detection environment and equipment, making them unable to be widely used in practice.…”

Section: Introductionmentioning

confidence: 99%

Gated Fusion Based Transformer Model for Crack Detection on Wind Turbine Blade

Tang,

Liu,

Zhang

2023

Energy Engineering

View full text Add to dashboard Cite

Harsh working environments and wear between blades and other unit components can easily lead to cracks and damage on wind turbine blades. The cracks on the blades can endanger the shafting of the generator set, the tower and other components, and even cause the tower to collapse. To achieve high-precision wind blade crack detection, this paper proposes a crack fault-detection strategy that integrates Gated Residual Network (GRN), a fusion module and Transformer. Firstly, GRN can reduce unnecessary noisy inputs that could negatively impact performance while preserving the integrity of feature information. In addition, to gain in-depth information about the characteristics of wind turbine blades, a fusion module is suggested to implement the information fusion of wind turbine features. Specifically, each fan feature is mapped to a one-dimensional vector with the same length, and all one-dimensional vectors are concatenated to obtain a two-dimensional vector. And then, in the fusion module, the information fusion of the same characteristic variables in the different channels is realized through the Channel-mixing MLP, and the information fusion of different characteristic variables in the same channel is realized through the Columnmixing MLP. Finally, the fused feature vector is input into the Transformer for feature learning, which enhances the influence of important feature information and improves the model's anti-noise ability and classification accuracy. Extensive experiments were conducted on the wind turbine supervisory control and data acquisition (SCADA) data from a domestic wind field. The results show that compared with other state-of-the-art models, including XGBoost, LightGBM, TabNet, etc., the F1-score of proposed gated fusion based Transformer model can reach 0.9907, which is 0.4%-2.09% higher than the compared models. This method provides a more reliable approach for the condition detection and maintenance of fan blades in wind farms.

show abstract

“…For example, a monitoring camera can be installed on the wind turbine [18], and then researchers apply hyperspectral imaging technology to find icing wind turbine blades [29]. Vibration signal analysis such as using changes in natural frequencies can contribute to detect damage for blades [41]. Besides, the Supervisory Control and Data Acquisition (SCADA) is a strong system for data acquisition and equipment monitoring, which is also the most widely used and technologically advanced in fault diagnosis of a large amount of wind power elements [35], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Icing Detection Model of Wind Turbine Blades Based on SCADA data

Jiang¹,

Jin²

2021

Preprint

View full text Add to dashboard Cite

Diagnosis of ice accretion on wind turbine blades is all the time a hard nut to crack in condition monitoring of wind farms. Existing methods focus on mechanism analysis of icing process, deviation degree analysis of feature engineering. However, there have not been deep researches of neural networks applied in this field at present. Supervisory control and data acquisition (SCADA) makes it possible to train networks through continuously providing not only operation parameters and performance parameters of wind turbines but also environmental parameters and operation modes. This paper explores the possibility that using convolutional neural networks (CNNs), generative adversarial networks (GANs) and domain adaption learning to establish intelligent diagnosis frameworks under different training scenarios. Specifically, PGANC and PGANT are proposed for sufficient and non-sufficient target wind turbine labeled data, respectively. The basic idea is that we consider a two-stage training with parallel GANs, which are aimed at capturing intrinsic features for normal and icing samples, followed by classification CNN or domain adaption module in various training cases. Model validation on three wind turbine SCADA data shows that two-stage training can effectively improve the model performance. Besides, if there is no sufficient labeled data for a target turbine, which is an extremely common phenomenon in real industrial practices, the addition of domain adaption learning makes the trained model show better performance. Overall, our proposed intelligent diagnosis frameworks can achieve more accurate detection on the same wind turbine and more generalized capability on a new wind turbine, compared with other machine learning models and conventional CNNs.

show abstract

Crack Detection of Fan Blade Based on Natural Frequencies

Cited by 14 publications

References 26 publications

Blade Crack Diagnosis Based on Blade Tip Timing and Convolution Neural Networks

Blade Crack Diagnosis Based on Blade Tip Timing and Convolution Neural Networks

Gated Fusion Based Transformer Model for Crack Detection on Wind Turbine Blade

Intelligent Icing Detection Model of Wind Turbine Blades Based on SCADA data

Contact Info

Product

Resources

About