Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with attention mechanism

Xiang, Ling; Wang, Penghe; Yang, Xin; Hu, Aijun; Su, Hao

doi:10.1016/j.measurement.2021.109094

Cited by 217 publications

(64 citation statements)

References 31 publications

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“…Here, particular sensor type(s) may be used in specific diagnostic applications, such as, e.g., the accelerometers are generally used to collect vibration signals from the WT drivetrain including bearings, gearbox, and shafts [84][85][86]. Similarly, microphones can be used to record acoustic emissions in harsh environments where it is difficult to implement accelerometers [87], and thermocouples can also be used for the same purpose as accelerometers [6,[88][89][90]. Finally, cameras can be used for metal deformation image recording [91,92].…”

Section: Data Acquisitionmentioning

confidence: 99%

“…This time-varying dynamicity can be affected by several constraints including the fatigue loading on faulty components, damage propagation, aging, and environmental conditions [4]. Therefore, considerable research exists that is aimed at moving towards advanced ML-based dynamic programming that is more suited to the nature of this process, rather than the ordinary offline learning [6]. Likewise, for some modes of operation, it is difficult to collect patterns sufficient for the prediction process, thus leading to engagement of knowledge from different sources, ranging from pre-hypotheses obtained from pre-trained learners or experts to generative models such as generative adversarial networks (GANs) and transfer learning (TL) [7].…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

et al. 2021

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Modern wind turbines operate in continuously transient conditions, with varying speed, torque, and power based on the stochastic nature of the wind resource. This variability affects not only the operational performance of the wind power system, but can also affect its integrity under service conditions. Condition monitoring continues to play an important role in achieving reliable and economic operation of wind turbines. This paper reviews the current advances in wind turbine condition monitoring, ranging from conventional condition monitoring and signal processing tools to machine-learning-based condition monitoring and usage of big data mining for predictive maintenance. A systematic review is presented of signal-based and data-driven modeling methodologies using intelligent and machine learning approaches, with the view to providing a critical evaluation of the recent developments in this area, and their applications in diagnosis, prognosis, health assessment, and predictive maintenance of wind turbines and farms.

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Section: Data Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

et al. 2021

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“…The weakness of this approach is that a lot of data will be needed to train the model, and the PETROVIETNAM user may not be able to be interpreted or described the extracted attributes. Several proven examples of machine learning tools used for feature extraction include CNN [21,22], autoencoder [22 -24], or principal component analysis [25 -27].…”

Section: Data Acquisitionmentioning

confidence: 99%

An overview of the application of machine learning in predictive maintenance

Trần

Trieu

Tran

et al. 2021

PVJ

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With the rise of industrial artificial intelligence (AI), smart sensing, and the Internet of Things (IoT), companies are learning how to use their data not only for analysing the past but also for predicting the future. Maintenance is a crucial area that can drive significant cost savings and production value around the world. Predictive maintenance (PdM) is a technique that collects, cleans, analyses, and utilises data from various manufacturing and sensing sources like machines usage, operating conditions, and equipment feedback. It applies advanced algorithms to the data, automatically compares the fed data and the information from previous cases to anticipate or predict equipment failure before it happens, thus helping optimise equipment utilisation and maintenance strategies, improve performance and productivity, and extend equipment life. Robust PdM tools enable organisations to leverage and maximise the value of their existing data to stay ahead of potential breakdowns or disruptions in services, and address them proactively instead of reacting to issues as they arise. Therefore, it has attracted more and more attention of specialists in recent years. This paper provides a comprehensive review of the recent advancements of machine learning (ML) techniques widely applied to PdM by classifying the research according to the ML algorithms, machinery and equipment used in data acquisition. Important contributions of the researchers are highlighted, leading to some guidelines and foundation for further studies. Currently, BIENDONG POC is running some pilot PdM projects for critical equipment in Hai Thach - Moc Tinh gas processing plant.

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“…Machine learning algorithms are widely used in wind turbine fault diagnosis. Xiang [15] used the method of convolutional neural network cascading to LSTM (long short-term memory) network to warn in the event of an abnormal state in wind turbines. Kordestani [16] combined the dynamic principal component analysis (DPCA) with the support vector machine (SVM) to identify dynamic fault states.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Wind Turbine Gearboxes Based on IWOA-RF

Tang

Liang

et al. 2021

Energies

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A fault diagnosis method for wind turbine gearboxes based on undersampling, XGBoost feature selection, and improved whale optimization-random forest (IWOA-RF) was proposed for the problem of high false negative and false positive rates in wind turbine gearboxes. Normal samples of raw data were subjected to undersampling first, and various features and data labels in the raw data were provided with importance analysis by XGBoost feature selection to select features with higher label correlation. Two parameters of random forest algorithm were optimized via the whale optimization algorithm to create a fitness function with the false negative rate (FNR) and false positive rate (FPR) as evaluation indexes. Then, the minimum fitness function value within the given scope of parameters was found. The WOA was controlled by the hyper-parameter α to optimize the step size. This article uses the variant form of the sigmoid function to alter the change trend of the WOA hyper-parameter α from a linear decline to a rapid decline first and then a slow decline to allow the WOA to be optimized. In the initial stage, a larger step size and step size change rate can make the model progress to the optimization target faster, while in the later stage of optimization, a smaller step size and step size change rate allows the model to more accurately find the minimum value of the fitness function. Finally, two hyper-parameters, corresponding to the minimum fitness function value, were substituted into a random forest algorithm for model training. The results showed that the method proposed in this paper can significantly reduce the false negative and false positive rates compared with other optimization classification methods.

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Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with attention mechanism

Cited by 217 publications

References 31 publications

Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

An overview of the application of machine learning in predictive maintenance

Fault Diagnosis Method for Wind Turbine Gearboxes Based on IWOA-RF

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