An enhanced sparse representation-based intelligent recognition method for planet bearing fault diagnosis in wind turbines

Kong, Yun; Qin, Zhaoye; Wang, Tianyang; Han, Qinkai; Chu, Fulei

doi:10.1016/j.renene.2021.04.019

Cited by 59 publications

(19 citation statements)

References 48 publications

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“…Moussa et al [114] proposed a digital twin model for large hydroelectric generators. To improve the accuracy and efficiency of prediction and health management of wind power, Tao et al [112,[180][181][182][183] proposed a digital twin failure prediction model for complex equipment, which effectively utilized the interaction mechanism of digital twin and data fusion techniques. Chen et al [184] proposed a future-oriented intelligent, semiautonomous human-cyber-physical system fusion wind turbine under this new concept, as shown in Figure 12.…”

Section: The Applicationsmentioning

confidence: 99%

Digital Twin for Integration of Design-Manufacturing-Maintenance: An Overview

Zhu

et al. 2022

Chin. J. Mech. Eng.

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Traditional design, manufacturing and maintenance are run and managed independently under their own rules and regulations in an increasingly time-and-cost ineffective manner. A unified platform for efficient and intelligent design-manufacturing-maintenance of mechanical equipment and systems is highly needed in this rapidly digitized world. In this work, the definition of digital twin and its research progress and associated challenges in the design, manufacturing and maintenance of engineering components and equipment were thoroughly reviewed. It is indicated that digital twin concept and associated technology provide a feasible solution for the integration of design-manufacturing-maintenance as it has behaved in the entire lifecycle of products. For this aim, a framework for information-physical combination, in which a more accurate design, a defect-free manufacturing, a more intelligent maintenance, and a more advanced sensing technology, is prospected.

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Section: The Applicationsmentioning

confidence: 99%

Digital Twin for Integration of Design-Manufacturing-Maintenance: An Overview

Zhu

et al. 2022

Chin. J. Mech. Eng.

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“…1–4 Therefore, monitoring and diagnosing the condition of the drive bearings of wind turbines is an essential way to ensure their safety and reliability, reduce their life-cycle maintenance costs, and extend their performance life. 5–12…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3][4] Therefore, monitoring and diagnosing the condition of the drive bearings of wind turbines is an essential way to ensure their safety and reliability, reduce their lifecycle maintenance costs, and extend their performance life. [5][6][7][8][9][10][11][12] Fault pattern identification is derived from fault diagnosis, which aims to identify and label the raw fault data without a priori knowledge and to establish a targeted fault pattern database of the wind turbine performance process. 10,[13][14][15][16][17] Traditional fault pattern identification is mainly divided into statistical-based methods and artificial intelligence-based methods, 18,19 such as fuzzy classifiers, [20][21][22] random forests (RFs), 23,24 and clustering methods including k-nearest neighbor (KNN) and spectral clustering.…”

Section: Introductionmentioning

confidence: 99%

A novel self-learning framework for fault identification of wind turbine drive bearings

Yuan

Liang

Wang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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The drive train is one of the core structures of a wind turbine, and its working condition seriously affects the performance quality. It is important to identify the fault pattern of the drive bearings in time to ensure the safety and reliability of the wind turbine. However, in traditional methods, offline modeling and online identification are often fragmented, and such a mechanism limits the adaptive updating of the model. To realize real-time updating and self-learning of the identification model, we proposed a novel self-learning framework for the intelligent fault identification of wind turbine drive bearings. First, a complete ensemble empirical mode decomposition with adaptive noise analysis–based quantification scheme for intrinsic mode function values is proposed. Then, based on the intrinsic mode function values, we offer an attention mechanism for fault feature identification and construct an initial fault pattern database using unsupervised clustering techniques. Second, abnormal data are identified by the proposed artificial immunity–based outlier detection algorithm to determine the type of immune response. Third, we design an automatic update strategy based on incremental learning to realize adaptive creation, deletion, and modification of fault patterns. The proposed intelligent framework is applied to the fault diagnosis of a real offshore wind turbine drive train, showing its advantages in intelligent fault identification and model updating.

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“…At the same time, some other intelligent methods have also been applied to bearing fault diagnosi. In order to solve fault of planetary bearing, Kong et al [15] proposed an intelligent recognition method based on enhanced sparse representation (ESRIR). Wang et al [16] explored a new sparse representation method that uses a new time-varying cosine-packet dictionary for the bearing fault diagnosis of wind turbines operating under varying speed condition that can adaptive to the variations of major frequencies of the vibration signals.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Method of Wind Turbine Based on Improved Anti-Noise Residual Shrinkage Network

Li¹

2022

Energy Engineering

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Aiming at the difficulty of rolling bearing fault diagnosis of wind turbine under noise environment, a new bearing fault identification method based on the Improved Anti-noise Residual Shrinkage Network (IADRSN) is proposed. Firstly, the vibration signals of wind turbine rolling bearings were preprocessed to obtain data samples divided into training and test sets. Then, a bearing fault diagnosis model based on the improved anti-noise residual shrinkage network was established. To improve the ability of fault feature extraction of the model, the convolution layer in the deep residual shrinkage network was replaced with a Dense-Net layer. To further improve the anti-noise ability of the model, the first layer of the model was set as the Drop-block layer. Finally, the labeled data samples were used for training model and the trained model was applied to the test set to output the fault diagnosis results. The results showed that the proposed method could achieve the fault diagnosis of wind turbine bearing more accurately in the high noise environment through comparison and verification.

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An enhanced sparse representation-based intelligent recognition method for planet bearing fault diagnosis in wind turbines

Cited by 59 publications

References 48 publications

Digital Twin for Integration of Design-Manufacturing-Maintenance: An Overview

Digital Twin for Integration of Design-Manufacturing-Maintenance: An Overview

A novel self-learning framework for fault identification of wind turbine drive bearings

Bearing Fault Diagnosis Method of Wind Turbine Based on Improved Anti-Noise Residual Shrinkage Network

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