Review and Perspectives of Machine Learning Methods for Wind Turbine Fault Diagnosis

Tang, Mingzhu; Zhao, Qi; Wu, Huawei; Wang, Ziming; Meng, Caihua; Wang, Yifan

doi:10.3389/fenrg.2021.751066

Cited by 17 publications

(3 citation statements)

References 116 publications

(107 reference statements)

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“…In fact, artificial intelligence (AI) is the main component of data-driven methods, and due to the considerable and fast-paced progress in AI and machine learning tools, as well as the increasing complexity of systems, data-driven techniques are drawing more and more attention. Machine learning is mainly divided into three groups: supervised, unsupervised and semi-supervised learning methods [107]. For supervised learning, a set of labelled data is required, and the training and learning processes are based on the labelled data to find the correlation between the input data and output.…”

Section: Data-driven Fdd Methods For Electric Motor Drivementioning

confidence: 99%

Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles

2023

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Fault detection and diagnosis (FDD) is of utmost importance in ensuring the safety and reliability of electric vehicles (EVs). The EV’s power train and energy storage, namely the electric motor drive and battery system, are critical components that are susceptible to different types of faults. Failure to detect and address these faults in a timely manner can lead to EV malfunctions and potentially catastrophic accidents. In the realm of EV applications, Permanent Magnet Synchronous Motors (PMSMs) and lithium-ion battery packs have garnered significant attention. Consequently, fault detection methods for PMSMs and their drives, as well as for lithium-ion battery packs, have become a prominent area of research. An effective FDD approach must possess qualities such as accuracy, speed, sensitivity, and cost-effectiveness. Traditional FDD techniques include model-based and signal-based methods. However, data-driven approaches, including machine learning-based methods, have recently gained traction due to their promising capabilities in fault detection. This paper aims to provide a comprehensive overview of potential faults in EV motor drives and battery systems, while also reviewing the latest state-of-the-art research in EV fault detection. The information presented herein can serve as a valuable reference for future endeavors in this field.

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Section: Data-driven Fdd Methods For Electric Motor Drivementioning

confidence: 99%

Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles

2023

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“…The development of a DT virtual model is one of the key technologies for FD and life prediction of HS using the DT method. Many numerical models and methods [1] have been gradually applied to FD and the life management of HS. To improve the accuracy of HS prediction and diagnosis, various methods have been applied, such as expert systems [2] , optimization method of support vector machine [3] , wavelet packet transformation [4] , machine learning [5][6] , transfer learning [7] , and so on.…”

Section: Introductionmentioning

confidence: 99%

Research on fault diagnosis method of the hydraulic system based on digital twin

Sun,

Yang,

Shi

et al. 2024

J. Phys.: Conf. Ser.

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As an advanced technology, digital-twin (DT) is an effective method to achieve information fusion and has been widely applied in fault diagnosis (FD). An integrated model, which consists of the DT model, consistency model, and FD network, is developed to detect faults in hydraulic systems (HS) by the dual effective information communication between the virtual model and real system. The consistency between the model and the real system can be guaranteed with the consistency model. A large number of labeled datasets generated by the DT model are used to train the FD network, which results in a significant reduction in the uncertainty of model parameters. A case study is done to investigate the performance of the DT method and the results show that the method can diagnose faults of HS effectively, efficiently, and accurately.

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“…In general, frosting type can be divided into condensation frosting and de-sublimation frosting based on the frosting mechanism. Since the de-sublimation frosting mainly occurs at an ultra-low surface temperature [11] or water vapor pressure [12] , condensation frosting is much more common in engineering applications. Besides, it is difficult to quantitatively investigate the condensation frosting characteristics on most of the frosting components in practical applications due to their complex structures.…”

Section: Introductionmentioning

confidence: 99%

Cold Plate Temperature Effect on Droplet and Frost Crystal Behaviors at the Early Condensation Frosting Stage Considering Plate Edge Effect

Zhang¹,

Song²,

Tianzhuo³

et al. 2022

Eng. Sci.

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The condensation frosting phenomenon commonly exists in engineering applications, and the relevant studies on flat plates with fixed surface temperatures are the most concerned in related fields. The existing studies usually focus on droplet condensation and frozen characteristics in the center region of cold plates and cannot reveal those near the plate edge. Therefore, a systematic study from droplet condensation to frost layer growth stages in the edge region of a cold plate was conducted in this paper. The experimental results showed that the frozen stage period of the edge-affected zone was significantly smaller than that of the unaffected zone. The ratio of average freezing wave propagation velocity in the edge-affected zone to that in the unaffected zone at plate temperatures of -9.0, -14.0, and -19.0 o C were 7. 70, 5.56, and 5.11, respectively. Besides, the area-average equivalent diameter and coverage area ratio of droplets decreased with a decrease in plate temperature at the end of their respective droplet frozen stages. This study may help to better understand temporal and spatial condensation frosting characteristics on a horizontal cold plate with right-angle edges, and guide the establishment of a staged and divisional frosting model on cold plates.

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Review and Perspectives of Machine Learning Methods for Wind Turbine Fault Diagnosis

Cited by 17 publications

References 116 publications

Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles

Fault Detection and Diagnosis of the Electric Motor Drive and Battery System of Electric Vehicles

Research on fault diagnosis method of the hydraulic system based on digital twin

Cold Plate Temperature Effect on Droplet and Frost Crystal Behaviors at the Early Condensation Frosting Stage Considering Plate Edge Effect

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