Condition-based maintenance for the offshore wind turbine based on long short-term memory network

Sun, Yu; Kang, Jichuan; Sun, Liping; Jin, Peng; Bai, Xu

doi:10.1177/1748006x20965434

Cited by 7 publications

(6 citation statements)

References 27 publications

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“…[15] proposed an LSTM to determine the fault location and estimate the RUL of the aero engine. More recently, several works [16], [17], [18], [19], [20], [21] have suggested LSTM-based approaches for RUL estimation, showing the efficacy of performing LSTM over RNN. As an improvement, another variant of LSTM was used by [22] is Bi-directional LSTM (BLSTM) that can learn the bi-directional temporal dependencies from sensor data for Aircraft Engine RUL estimation.…”

Section: A Data-driven Methods For Rul Estimationmentioning

confidence: 99%

RUL Prediction Using a Fusion of Attention-Based Convolutional Variational AutoEncoder and Ensemble Learning Classifier

et al. 2023

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Predicting the remaining useful life (RUL) is a critical step before the decision-making process and developing maintenance strategies. As a result, it is frequently impacted by uncertainty in a practical context and may cause issues. This paper proposes a new hybrid deep architecture that predicts when an in-service machine will fail to overcome the latter problem, allowing for an improved data analysis and dimensionality reduction capability providing better spatial distributions of features and increasing interpretability. A deep Convolutional Variational AutoEncoder with an Attention mechanism (ACVAE) has been developed and tested using the aero-engine C-MAPSS dataset. We defined two adapted threshold settings (α1, α2) by analysing the spatial distribution and minimizing the overlapping area between the degradation classes. To reduce the conflict zone, we used the soft voting classifier. The performance of our visual explainable deep learning model has reached a higher level of accuracy compared with previous existing models.

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Section: A Data-driven Methods For Rul Estimationmentioning

confidence: 99%

RUL Prediction Using a Fusion of Attention-Based Convolutional Variational AutoEncoder and Ensemble Learning Classifier

et al. 2023

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“…The decision‐making in CBM is based on device information collected (sensors for instance) and reported through condition monitoring process, see Refs. 18 and 19. This is because many systems show signs, conditions, and indications before their actual failure 20 .…”

Section: Literature Reviewmentioning

confidence: 99%

Two‐threshold control limit policy in condition‐based maintenance

Hanbali

Saleh

Ullah

2022

Quality & Reliability Eng

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In this paper, we consider a component exhibiting a continuous monotone degradation modeled as a Gamma process. The maintenance policy is of condition‐based type with periodic inspections. It follows the two threshold control limit policy with a preventive maintenance limit, a switching limit, and a given failure limit. Our interest is to minimize the annual expected maintenance cost subject to a constraint on the average availability. The decision variables are the preventive limit, the switching limit, and the inspections schedule. We derive exact results for the key performance indicators, but since they are numerically involved, we propose a simple, that is, involving necessary spreadsheet calculations, accurate approximation. We apply our model to a case from the process industry, namely, a carbon steel hydrogen dryer consisting of a cylindrical pressure vessel. This vessel suffers from corrosion with thickness growth modeled as a Gamma process. The optimization results show that although the annual maintenance cost increases with the mean and the coefficient of variation of the yearly corrosion thickness, its mean has a higher impact on cost. Based on the cost sensitivity analysis, we find that the impact of a vast increase of the corrective maintenance cost for the same inspection and preventive costs can be mitigated by appropriately reducing the corrective maintenance probability. Finally, we empirically show that the two threshold policy achieves an annual cost savings up to 12.3%$12.3\%$ compared to the single threshold policy.

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“…To reduce O&M costs and enhance system reliability, condition monitoring (CM), fault diagnosis, and prognosis are of prior importance through the detection of certain faults before they reach catastrophic fault severity levels. Hence, O&M costs can be decreased along with maintenance interval optimisation [9].…”

Section: Introductionmentioning

confidence: 99%

Wind Turbine Predictive Fault Diagnostics Based on a Novel Long Short-Term Memory Model

Zhang,

Robinson,

Basu

2023

Algorithms

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The operation and maintenance (O&M) issues of offshore wind turbines (WTs) are more challenging because of the harsh operational environment and hard accessibility. As sudden component failures within WTs bring about durable downtimes and significant revenue losses, condition monitoring and predictive fault diagnostic approaches must be developed to detect faults before they occur, thus preventing durable downtimes and costly unplanned maintenance. Based primarily on supervisory control and data acquisition (SCADA) data, thirty-three weighty features from operational data are extracted, and eight specific faults are categorised for fault predictions from status information. By providing a model-agnostic vector representation for time, Time2Vec (T2V), into Long Short-Term Memory (LSTM), this paper develops a novel deep-learning neural network model, T2V-LSTM, conducting multi-level fault predictions. The classification steps allow fault diagnosis from 10 to 210 min prior to faults. The results show that T2V-LSTM can successfully predict over 84.97% of faults and outperform LSTM and other counterparts in both overall and individual fault predictions due to its topmost recall scores in most multistep-ahead cases performed. Thus, the proposed T2V-LSTM can correctly diagnose more faults and upgrade the predictive performances based on vanilla LSTM in terms of accuracy, recall scores, and F-scores.

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Condition-based maintenance for the offshore wind turbine based on long short-term memory network

Cited by 7 publications

References 27 publications

RUL Prediction Using a Fusion of Attention-Based Convolutional Variational AutoEncoder and Ensemble Learning Classifier

RUL Prediction Using a Fusion of Attention-Based Convolutional Variational AutoEncoder and Ensemble Learning Classifier

Two‐threshold control limit policy in condition‐based maintenance

Wind Turbine Predictive Fault Diagnostics Based on a Novel Long Short-Term Memory Model

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