Prognostics and Health Management of Renewable Energy Systems: State of the Art Review, Challenges, and Trends

Saïdi, Lotfi; Benbouzid, Mohamed

doi:10.3390/electronics10222732

Cited by 15 publications

(9 citation statements)

References 43 publications

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“…In this context, explainable artificial intelligence algorithms have raised to explain the predictions, such as local interpretable model-agnostic explanations or Shapley additive explanation methods, which help hybrid models to gain explainability [49]. Moreover, hybrid models effectively handle uncertainties, yet their high algorithmic complexity and reliance on physical modeling for degradation phenomena can be resource-intensive [9].…”

Section: Prognostics Rul Estimationmentioning

confidence: 99%

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Challenges on prognostics and health management for wind turbine components

Cuesta,

Leturiondo,

Vidal

et al. 2024

J. Phys.: Conf. Ser.

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This paper explores the applicability of prognostics and health management (PHM) for wind turbines (WTs), presenting the PHM approach along with challenges and opportunities in the context of WT components. First, the PHM framework is introduced, consisting of three blocks: observation, analysis, and action. Critical components and failure modes for WTs are identified, and data acquisition strategies using supervisory control and data adquisition (SCADA) and condition monitoring (CM) data are discussed. Prognostics, specifically remaining useful life (RUL) estimation, employs physics model-based, data-driven, and hybrid models. Finally, challenges and opportunities related to data, analysis and CM, and developing RUL prediction models have been found. Data challenges include data standardization, limited public datasets, and data quality issues. Analysis and CM challenges address new sensorless and non-intrusive techniques, as well as the fusion of data sources. Prognostics model challenges involve uncertainty management, interpretability issues, and the need for online updates. Addressing challenges requires incorporating physical knowledge, utilizing transfer learning, and improving online RUL prediction methods.

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Section: Prognostics Rul Estimationmentioning

confidence: 99%

“…To achieve these goals, a PHM framework is shown in Figure 1, slightly different from the provided in the literature [6,9,17,18]. The introduced PHM framework consists of ten modules divided into three blocks: observation block, analysis block and action block, which presents two modifications from the previous works presented.…”

Section: Introductionmentioning

confidence: 99%

Challenges on prognostics and health management for wind turbine components

Cuesta,

Leturiondo,

Vidal

et al. 2024

J. Phys.: Conf. Ser.

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“…The precision and applicability of model-based models on the component level are very high, but on the system level is low [16]. Model-based models do not have a rigid demand on the amount of data, however, running high fidelity models of a complex system is time-consuming [8]. Datadriven models capture system behaviors by data features without too much prior knowledge about the system.…”

Section: Strengths and Weaknesses Of Modelsmentioning

confidence: 99%

“…Fault diagnostics is to distinguish the fault, such as its type, magnitude, location, and time [6]. Failure prognostics is predicting remaining useful life (RUL), which describes how long the asset or system runs from the current state to the state where it reaches the threshold of repair or failure [7,8]. With the accurate forecast of RUL, necessary maintenance can be scheduled in advance to minimize O&M expenses.…”

Section: Introductionmentioning

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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“…With respect to the sequence vibration signal of the bearing, these vibration data have merely small fluctuations in long time series. Until the end of the bearing's life, the vibration fluctuates dramatically, which is often difficult to predict; hence, the existing models cannot obtain satisfactory prediction results [9].…”

Section: Introductionmentioning

confidence: 99%

SACGNet: A Remaining Useful Life Prediction of Bearing with Self-Attention Augmented Convolution GRU Network

Duan

Chen³

et al. 2022

Lubricants

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In recent years, the development of deep learning-based remaining useful life (RUL) prediction methods of bearings has flourished because of their high accuracy, easy implementation, and lack of reliance on a priori knowledge. However, there are two challenging issues concerning the prediction accuracy of existing methods. The run-to-failure sequential data and its RUL labels are almost inaccessible in real-world scenarios. Meanwhile, the existing models usually capture the general degradation trend of bearings while ignoring the local information, which restricts the model performance. To tackle the aforementioned problems, we propose a novel health indicator derived from the original vibration signals by combining principal components analysis with Euclidean distance metric, which was motivated by the desire to resolve the dependency on RUL labels. Then, we design a novel self-attention augmented convolution GRU network (SACGNet) to predict the RUL. Combining a self-attention mechanism with a convolution framework can both adaptively assign greater weights to more important information and focus on local information. Furthermore, Gated Recurrent Units are used to parse the long-term dependencies in weighted features such that SACGNet can utilize the important weighted features and focus on local features to improve the prognostic accuracy. The experimental results on the PHM 2012 Challenge dataset and the XJTU-SY bearing dataset have demonstrated that our proposed method is superior to the state of the art.

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Prognostics and Health Management of Renewable Energy Systems: State of the Art Review, Challenges, and Trends

Cited by 15 publications

References 43 publications

Challenges on prognostics and health management for wind turbine components

Challenges on prognostics and health management for wind turbine components

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

SACGNet: A Remaining Useful Life Prediction of Bearing with Self-Attention Augmented Convolution GRU Network

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