Two-Stage Condition-based Maintenance Model of Wind Turbine: from Diagnosis to Prognosis

Baboli, Payam Teimourzadeh; Raeiszadeh, Amin; Babazadeh, Davood; Meiners, Jens-Christian

doi:10.1109/isc251055.2020.9239029

Cited by 6 publications

(1 citation statement)

References 19 publications

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“…Baboli et al construct a three-layer ANN model to predict the RUL by the normal temperature of four key components: gearbox, converter, generator, and transformer. The mean absolute errors (MAE) of temperature are 2.6 • C (gearbox), 3.2 • C (converter), 2.9 • C (generator) and 3.8 • C (transformer) [52]. Senanayaka et al use RNN with LSTM units to predict the RUL of 5 bearings and errors for 3 bearings are smaller than 30 % [53].…”

Section: Machine Learning Model ML Model Is a Prediction Model With M...mentioning

confidence: 99%

A review of failure prognostics for predictive maintenance of offshore wind turbines

Zhang

Vatn

Rasheed³

2022

J. Phys.: Conf. Ser.

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Offshore wind turbines (OWTs) are important facilities for wind power generation because of their low land use and high electricity output. However, the harsh environment and remote location of offshore sites make it difficult to conduct maintenance on turbines. To upkeep OWTs cost-effectively, predictive maintenance (PdM) is an appealing strategy for offshore wind industry. The heart of PdM is failure prognostics, which aims to predict an asset’s remaining useful life (RUL) based on condition monitoring (CM). To provide references to PdM of OWTs, this paper presents a systematic review of failure prognostic models for wind turbines. In this review, data-driven models, model-based models, and hybrid models are classified and presented for model selection. The findings reveal that it is promising to develop hybrid models in the future and combine the advantages of data-driven and model-based models. Currently, the internal combinations of machine learning methods and statistical approaches in data-driven models are more common than exterior linkages between data-driven models and model-based models. The limitations and strengths of different models are discussed, and opportunities for developing hybrid models are highlighted in the conclusion.

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Section: Machine Learning Model ML Model Is a Prediction Model With M...mentioning

confidence: 99%