A trend cloud model-based approach for the identification of wind turbine gearbox anomalies

Fang, Ruiming; Shang, Rong; Jiang, Shunhui; Peng, Changqing; Ye, Zhijun

doi:10.3233/jifs-169599

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…As mentioned, the gearbox is one of the WT components that most frequently appeared in the reviewed articles as it generates a high failure rate and causes long downtime in the WT [95]. For example, in [48][49][50], different AI techniques such as support vector regression (SVR), SVM, and DNN were used in CM to identify anomalies and predict gearbox failures from temperature and oil pressure data acquired by the SCADA system of the gearbox. Moreover, another approach to predict the faults and remaining useful life of gearboxes was presented in [58], where the failure was predicted up to a month before it occurred, using ML techniques on large amounts of SCADA data.…”

Section: Rq2: What Are the Main Components And/or Subsystems Of The Wts To Which CM Techniques Are Applied?mentioning

confidence: 99%

Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review

et al. 2020

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Operation and maintenance (O&M) activities represent a significant share of the total expenditure of a wind farm. Of these expenses, costs associated with unexpected failures account for the highest percentage. Therefore, it is clear that early detection of wind turbine (WT) failures, which can be achieved through appropriate condition monitoring (CM), is critical to reduce O&M costs. The use of Supervisory Control and Data Acquisition (SCADA) data has recently been recognized as an effective solution for CM since most modern WTs record large amounts of parameters using their SCADA systems. Artificial intelligence (AI) techniques can convert SCADA data into information that can be used for early detection of WT failures. This work presents a systematic literature review (SLR) with the aim to assess the use of SCADA data and AI for CM of WTs. To this end, we formulated four research questions as follows: (i) What are the current challenges of WT CM? (ii) What are the WT components to which CM has been applied? (iii) What are the SCADA variables used? and (iv) What AI techniques are currently under research? Further to answering the research questions, we identify the lack of accessible WT SCADA data towards research and the need for its standardization. Our SLR was developed by reviewing more than 95 scientific articles published in the last three years.

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Section: Rq2: What Are the Main Components And/or Subsystems Of The Wts To Which CM Techniques Are Applied?mentioning

confidence: 99%

Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review

et al. 2020

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Condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network

Liu

Wang

Xie

et al. 2023

Engineering Applications of Artificial Intelligence

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Randomness-oriented Multi-dimensional Cloud-based belief rule Base approach for complex system modeling

Chang

Zhang

2022

Expert Systems with Applications

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A trend cloud model-based approach for the identification of wind turbine gearbox anomalies

Cited by 5 publications

References 14 publications

Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review

Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review

Condition monitoring of wind turbines with the implementation of spatio-temporal graph neural network

Randomness-oriented Multi-dimensional Cloud-based belief rule Base approach for complex system modeling

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