Fault Diagnosis for Wind Turbine Gearboxes by Using Deep Enhanced Fusion Network

Pu, Ziqiang; Li, Chuan; Zhang, Shaohui; Bai, Yun

doi:10.1109/tim.2020.3024048

Cited by 31 publications

(16 citation statements)

References 31 publications

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“…To detect anomalies in the wind turbine gearbox, Twin Support Vector Machine (TWSVM) and an adaptive threshold were used in [7]. To extract features from three-axial vibration data for fault diagnosis of a wind turbine gearbox, a Deep Enhanced Fusion Network (DEFN) was used in [8]. The deep joint variational autoencoder method was used in conjunction with wind farm supervisory control and data acquisition to diagnose faults in the wind turbine gearbox in [9].…”

Section: Related Workmentioning

confidence: 99%

A Fault Diagnosis Technique for Wind Turbine Gearbox: An Approach using Optimized BLSTM Neural Network with Undercomplete Autoencoder

Sreenatha

Mallikarjuna²

2023

Eng. Technol. Appl. Sci. Res.

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The gearbox is one of the critical components of a wind turbine. Proactive maintenance of wind turbine gearboxes is crucial to decrease maintenance and operational costs and the long downtime of the complete system. As the gearbox is a significant part of the wind turbine, a fault in the gearbox leads to the breakdown of the wind turbine system. Hence, it is important to study and analyze the faults in wind turbine gearbox systems. In this article, a neural network-based model, a Bidirectional Long Short-Term Memory (BLSTM) fused with an autoencoder is intended to categorize the condition of the gearbox into good or bad (broken tooth) condition. Feature learning and reduction are achieved extensively through the autoencoder. This improves the performance of the BLSTM model regarding time complexity and classification accuracy. This model has been applied with time series vibration data of the gearbox in a wind turbine system. The suggested model's performance is analyzed using an openly available wind turbine gearbox vibration dataset. The result showed that BLSTM accuracy with an under-complete autoencoder is highly robust and appropriate for the health monitoring of wind turbine gearbox systems using time series data. Also, in order to illustrate the advantage of the projected model for fault analysis and diagnosis in wind turbine gearbox, the throughput or time complexity of training and testing of the split dataset is compared with the conventional BLSTM model.

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Section: Related Workmentioning

confidence: 99%

A Fault Diagnosis Technique for Wind Turbine Gearbox: An Approach using Optimized BLSTM Neural Network with Undercomplete Autoencoder

Sreenatha

Mallikarjuna²

2023

Eng. Technol. Appl. Sci. Res.

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“…Du et al [122] proposed a fault diagnosis method on the basis of the union of redundant dictionary for wind turbine gearboxes, in which an adaptive feature identification method was used to extract multiple components from the superimposed signals. Pu et al [123] proposed a deep enhanced fusion network (DEFN)-based fault diagnosis method for wind turbine gearboxes, in which the fused three-axis features were used to train the DEFN model. In [122,123], the scalability and generality of the algorithm should be considered in future.…”

Section: Monitoring For Offshore Wind Turbinesmentioning

confidence: 99%

“…Pu et al [123] proposed a deep enhanced fusion network (DEFN)-based fault diagnosis method for wind turbine gearboxes, in which the fused three-axis features were used to train the DEFN model. In [122,123], the scalability and generality of the algorithm should be considered in future. Lu et al [124] proposed a current-based fault diagnosis method for drivetrain gearboxes, in which a statistical analysis algorithm was used to extract the fault features from the nonstationary stator current signals; nevertheless, the fault type identification, different fault locations, and the remaining useful life prediction should also be considered.…”

Section: Monitoring For Offshore Wind Turbinesmentioning

confidence: 99%

Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms

Kou

Zhang

et al. 2022

Sensors

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In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean, renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind power has been developing in the direction of digitization and intelligence. It is of great significance to carry out research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit for the reduction of the operation and maintenance costs, the improvement of the power generation efficiency, improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms. This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of “offshore wind power engineering and biological and environment”, the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored, especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of power equipment, and digital platforms.

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“…17 Pu et al proposed a deep augmented fusion network, which uses three sparse self-coding networks to extract fault features, a finite element framework to fuse the features, and finally an ESN network for diagnosis. 18 Yang et al proposed a depth joint variable auto-encoder detection method, which can reach high accuracy with a very small error rate machine. 19 In summary, most of the faults can already be recognized effectively.…”

Section: Introductionmentioning

confidence: 99%

A new class of fault recognition method for wind turbine systems based on deep learning

Wang,

Liu,

Tong

et al. 2023

Adaptive Control & Signal

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SummaryConventional fault recognition algorithms can only recognize the classes of faults that have appeared in wind turbine systems. However, if a new category of faults appears, the traditional algorithm can only misclassify it into the class of pre‐existing faults. In this paper, a new class fault recognition method based on deep learning is proposed. Firstly, the initialized model is built using known fault data, including detectors and classifiers. Secondly, the new class fault data detected by the detectors are put into the cache, and when the cache overflows, the new class faults are augmented with data using generative adversarial networks. Finally, the new class fault data and the generated data are added to the initial training set, and the structure and weights of the initialized model are updated using the new training set. In this way, the purpose of recognizing the new class of faults is achieved. The experimental results show that the proposed detection algorithm can effectively detect new class faults and the model can efficiently solve the problem of the new class of fault recognition after data augmentation.

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Fault Diagnosis for Wind Turbine Gearboxes by Using Deep Enhanced Fusion Network

Cited by 31 publications

References 31 publications

A Fault Diagnosis Technique for Wind Turbine Gearbox: An Approach using Optimized BLSTM Neural Network with Undercomplete Autoencoder

A Fault Diagnosis Technique for Wind Turbine Gearbox: An Approach using Optimized BLSTM Neural Network with Undercomplete Autoencoder

Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms

A new class of fault recognition method for wind turbine systems based on deep learning

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