An integrated methodology for estimating the remaining useful life of high-speed wind turbine shaft bearings with limited samples

Merainani, Boualem; Laddada, S.; Bechhoefer, Eric; Chikh, Mohamed Abdessamed Ait; Benazzouz, Djamel

doi:10.1016/j.renene.2021.10.062

Cited by 16 publications

(9 citation statements)

References 31 publications

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“…ANFIS can predict the RUL of bearings by vibration data from run-to-failure tests [55,56]. ENN is applied to predict the RUL of HSSB and predict the RUL over 20 to 35 days in the future [57][58][59]. When ANFIS and NN are applied to predict the degradation of insulated gate bipolar transistor, prediction accuracy reaches 80.96% (NN) and 68.09% (ANFIS) [60].…”

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%

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

Zhang

Vatn

Rasheed³

2022

J. Phys.: Conf. Ser.

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“…Moreover, the method operates on a single degradation feature and this feature reaches a constant end value for every run-to-failure sequence, limiting the method's potential for more complex cases with signals, which are either multivariate or have varying value ranges per run-to-failure sequence. Two papers deal with the RUL prediction of wind turbine bearings [27], [48] from an incomplete life cycle sequence. The former uses a state-space model constructed from an empirical equation for bearing wear based on the spalling area propagation.…”

Section: B Rul Prediction From Limited Training Datamentioning

confidence: 99%

“…As another hybrid approach, it is limited in its application to rolling element bearings. In the latter study [27], an Elman NN is used to obtain a data-driven condition model instead. However, the main limitation of both studies is that just a single run-to-failure sequence is used for demonstration and validation, such that its generalisation ability remains questionable.…”

Section: B Rul Prediction From Limited Training Datamentioning

confidence: 99%

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Data-Efficient Estimation of Remaining Useful Life for Machinery With a Limited Number of Run-to-Failure Training Sequences

2022

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Prognostics and Health Monitoring (PHM) of machinery is a research area with great relevance to industrial applications as it can serve as a foundation for safer, more cost-efficient operation and maintenance. The prediction of Remaining Useful Life (RUL) plays an important part in this field and has seen significant advances from the introduction of machine learning methods. However, these methods typically require model training with a large number of run-to-failure sequences, which are often not feasible to obtain due to the required time and cost investments. The present study addresses this issue by introducing a novel methodology, which first quantifies the deviation from the machine's health and fault state and then calculates a machine Health Index (HI) prior to the prediction of RUL. In addition, the start of a degradation state is determined. Alternative implementations of the proposed methodology are compared utilising several methods, including Support Vector Regression (SVR), Long Short-Term Memory (LSTM) Neural Network (NN), Mahalanobis Distance (MD), and LSTM Autoencoder (AE) NN. The methodology is applied to the open turbofan degradation (C-MAPSS) and bearing vibration (FEMTO-ST PROGNOSTIA) datasets. When a reduced subset of training sequences is used, the prediction results demonstrate that the proposed methodology largely outperforms the baseline method without HI generation. For example, when comparing prediction errors of the C-MAPSS dataset at a reduction of the available number of training sequences to 5%, the proposed method shows an average prediction improvement by 6.5% -19.2% relative to the baseline method. The presented approach is therefore suitable to improve model generalisation for cases with a limited number of training sequences. When the full training set is utilised, the most resource-saving variant of the proposed approach achieves an average training duration of 8.9% compared to the baseline method. Hence, an additional contribution of the presented data-efficient approach is the reduction of required computing resources, which has implications on training time, energy consumption, and environmental impact.

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“…In [17], the adaptive-neuro fuzzy inference system and the neo-fuzzy neuron were used to predict the RUL of rolling bearings. In [18], an Elman neural network was used for the RUL estimation of high-speed bearings in a wind turbine. In addition to the neural network-related models, the support vector machine (SVM) and its variants are also applied to life prediction, such as the achievements in [15,19,20].…”

Section: Introductionmentioning

confidence: 99%

A model for remaining useful life prediction of rolling bearings based on the IBA-FELM algorithm

Zhang,

Dai,

Xiao

2023

Meas. Sci. Technol.

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Rolling bearings are essential supporting components for most rotating machinery and are commonly placed at great risk of sudden failure. Accurate prediction of the remaining service life of rolling bearings is essential for ensuring reliable operation and establishing an effective maintenance strategy. Focusing on the extreme learning machine (ELM) methodology, an innovative predictive model with error feedback neuron integration is established to eliminate the deficiency in model generalization capability. To further improve the predictive accuracy, an improved bat algorithm (IBA) is introduced into the FELM model, in which the Levy flight and frequency influence factor are embedded into the traditional BA algorithm to enhance the parameter searching ability. Inverse hyperbolic function-based statistical indicators are proposed and verified by comparing with the classical RMS curve of full-life data, whose cosine similarity and correlation coefficient both exceed 0.95. Two sets of accelerated life experiments were selected to validate the effectiveness of the proposed IBA-FELM model. The results show that the integrated model can obtain high prediction accuracy and satisfactorily fit the real-life data. The maximal prediction error can be reduced from 1.57 to 0.0401 for experimental Case 1, and from 0.7375 to 0.1492 for Case 2. Compared with the other machine learning models, such as SVR, CNN, and LSTM networks, the IBA-FELM model also presents stronger optimization ability, higher generalization performance, and operation stability.

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An integrated methodology for estimating the remaining useful life of high-speed wind turbine shaft bearings with limited samples

Cited by 16 publications

References 31 publications

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

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

Data-Efficient Estimation of Remaining Useful Life for Machinery With a Limited Number of Run-to-Failure Training Sequences

A model for remaining useful life prediction of rolling bearings based on the IBA-FELM algorithm

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