Planetary Gear Faults Detection in Wind Turbine Gearbox Based on a Ten Years Historical Data From Three Wind Farms

Kordestani, Mojtaba; Rezamand, Milad; Orchard, Marcos E.; Carriveau, Rupp; Ting, David S.‐K.; Saif, Mehrdad

doi:10.1016/j.ifacol.2020.12.2767

Cited by 19 publications

(9 citation statements)

References 25 publications

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“…However, in the open real world, the distribution of data categories is often long-tailed, in which the number of training samples per class varies significantly from thousands of images to few samples. For example, in the scenarios such as railway traffic, mesothelioma diagnosis, and industrial fault detection [ 3 , 4 ], we need to detect an unexpected object where the real samples for the category of unexpected object are usually hard to collect, which leads to a long-tailed data distribution. There are many works [ 5 , 6 ] proposed to solve such real-world classification problems.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Relieving the Incompatibility of Network Representation and Classification for Long‐Tailed Data Distribution

Gao

Ming-sheng

2021

Computational Intelligence and Neuroscience

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In the real-world scenario, data often have a long-tailed distribution and training deep neural networks on such an imbalanced dataset has become a great challenge. The main problem caused by a long-tailed data distribution is that common classes will dominate the training results and achieve a very low accuracy on the rare classes. Recent work focuses on improving the network representation ability to overcome the long-tailed problem, while it always ignores adapting the network classifier to a long-tailed case, which will cause the “incompatibility” problem of network representation and network classifier. In this paper, we use knowledge distillation to solve the long-tailed data distribution problem and fully optimize the network representation and classifier simultaneously. We propose multiexperts knowledge distillation with class-balanced sampling to jointly learn high-quality network representation and classifier. Also, a channel activation-based knowledge distillation method is also proposed to improve the performance further. State-of-the-art performance on several large-scale long-tailed classification datasets shows the superior generalization of our method.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Relieving the Incompatibility of Network Representation and Classification for Long‐Tailed Data Distribution

Gao

Ming-sheng

2021

Computational Intelligence and Neuroscience

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“…Among them, the datadriven approaches usually do not rely on accurate mathematical models, but directly extract the latent information from historical data sets [5]. Therefore, the data-driven approaches were mostly used in sensors' FDI, such as the PCA methods [6][7][8], time-frequency analysis methods [9,10], machine learning-based methods [11][12][13][14][15][16][17], and the combination of the above methods [18][19][20][21][22]. Huang et al defined the isolation index by setting each sensor in turn as a missing variable and then recalculating the corresponding fault detection statistic.…”

Section: Introductionmentioning

confidence: 99%

“…Kordestani et al used the DPCA algorithm to reduce the data dimension of the vibration signal and used the support vector machine (SVM) to detect and isolate the gear faults in wind turbines. The analysis of the historical data has indicated the effectiveness of the algorithm [20]. And Li and Qu combined the convolutional neural network (CNN) and PCA methods to solve the problem of aeroengine sensors fault diagnosis, which could improve the efficiency of CNN and the accuracy of fault diagnosis [21].…”

Section: Introductionmentioning

confidence: 99%

Closed-Loop Fault Diagnosis of SDR Using Gap Metric, PCA, and Kalman’s Principle

Wang

Zeng

Wang

et al. 2023

International Journal of Aerospace Engineering

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The measurement of the pressure in the gas generator (GG) plays a decisive role in the closed-loop regulation of the gas flow in a solid ducted rocket ramjet (SDR). Therefore, the fault detection and isolation (FDI) of the pressure sensors is particularly significant. Especially in the GG with double pressure feedback, there are issues such as less available data and sensor fault transmission, which make the FDI more difficult. In this paper, for the GG with double pressure feedback, firstly, the “Consistency Index” was constructed based on the principal component analysis (PCA) algorithm, which made it easier to evaluate the consistency of the measured values. Secondly, based on Kalman’s principle, the redundancy information of the system was used to estimate the pressure in the GG. Finally, the gap metric between the measured pressure and the estimated pressure was employed to characterize the health of the sensors. Compared with the MWPCA algorithm, it was shown that our proposed algorithm was more accurate in fault diagnosis and could avoid the problem of missing alarm when two sensors had consistent faults, which would provide strong support for the safe operation of the SDR and could further promote its application in long-endurance aircrafts.

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“…The existing method of monitoring the health system of WTs are roughly classified into two categories: physical-based approaches and machine learning approaches. Back in the past, the conventional way is by the physical-based approach, which is being physically present on the site to monitor the wind turbine systems [98]. The accuracy of this method may be limited due to insufficient knowledge on specific domains or mistakenly analyzed the components, or even might have overlooked the system.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Technique Using Support Vector Machine in Wind Turbine Fault Diagnosis

Shahrulhisham

Chong²,

Yaw³

et al. 2022

J. Phys.: Conf. Ser.

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Wind energies are one of the most used resources worldwide and favours the economy by not emitting harmful gases that could lead to global warming. It is a cost-efficient method and environmentally friendly. Hence, explains the popularity of wind energy production over the years. Unfortunately, a minor fault could be contagious by affecting the nearby components, then a more complicated problem might arise, which may be costly. Thus, this article conducted a machine learning technique, support vector machine (SVM) to monitor the health of the wind turbine system by classifying the class of healthy data and faulty data. Some SVM types were experimented with, including Linear, Quadratic, Cubic, Fine Gaussian, Medium Gaussian, and Coarse Gaussian. Then these models were trained under different validation schemes that are cross-validation, holdout validation, and re-substitution validation as an approach to evaluate the performance of each model. In the end, Cubic SVM is proven to outperformed other models under the provision of 10-fold cross-validation with an accuracy of 98.25%. The result showed that Cubic SVM has the best performance while Linear SVM has the least accuracy among other models. Hence choosing the default value is preferred as the final product to diagnose the fault in wind turbine systems.

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Planetary Gear Faults Detection in Wind Turbine Gearbox Based on a Ten Years Historical Data From Three Wind Farms

Cited by 19 publications

References 25 publications

[Retracted] Relieving the Incompatibility of Network Representation and Classification for Long‐Tailed Data Distribution

[Retracted] Relieving the Incompatibility of Network Representation and Classification for Long‐Tailed Data Distribution

Closed-Loop Fault Diagnosis of SDR Using Gap Metric, PCA, and Kalman’s Principle

Application of Machine Learning Technique Using Support Vector Machine in Wind Turbine Fault Diagnosis

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