Comparison of New Anomaly Detection Technique for Wind Turbine Condition Monitoring Using Gearbox SCADA Data

McKinnon, Conor; Carroll, J. Douglas; McDonald, Alasdair; Koukoura, Sofia; Infield, David; Soraghan, Conaill

doi:10.3390/en13195152

Cited by 43 publications

(27 citation statements)

References 29 publications

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“…A large selection of Machine Learning algorithms can also be used for anomaly detection. McKinnon et al have studied the performances in condition monitoring of a gearbox of three popular algorithms: Isolation Forest (IF), One Class Support Vector Machine (OCSVM) and Elliptical Envelope (EE) and found that depending on the conditions OCSVM and IF reach best results [ 33 ]. Purarjomandlangrudi et al used Support Vector Machine (SVM) to process previously extracted features of the data for early detection of anomalies [ 34 ].…”

Section: Previous Workmentioning

confidence: 99%

An Ensemble Learning Solution for Predictive Maintenance of Wind Turbines Main Bearing

Beretta

Julian²,

Sepúlveda³

et al. 2021

Sensors

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A novel and innovative solution addressing wind turbines’ main bearing failure predictions using SCADA data is presented. This methodology enables to cut setup times and has more flexible requirements when compared to the current predictive algorithms. The proposed solution is entirely unsupervised as it does not require the labeling of data through work orders logs. Results of interpretable algorithms, which are tailored to capture specific aspects of main bearing failures, are merged into a combined health status indicator making use of Ensemble Learning principles. Based on multiple specialized indicators, the interpretability of the results is greater compared to black-box solutions that try to address the problem with a single complex algorithm. The proposed methodology has been tested on a dataset covering more than two year of operations from two onshore wind farms, counting a total of 84 turbines. All four main bearing failures are anticipated at least one month of time in advance. Combining individual indicators into a composed one proved effective with regard to all the tracked metrics. Accuracy of 95.1%, precision of 24.5% and F1 score of 38.5% are obtained averaging the values across the two windfarms. The encouraging results, the unsupervised nature and the flexibility and scalability of the proposed solution are appealing, making it particularly attractive for any online monitoring system used on single wind farms as well as entire wind turbine fleets.

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Section: Previous Workmentioning

confidence: 99%

An Ensemble Learning Solution for Predictive Maintenance of Wind Turbines Main Bearing

Beretta

Julian²,

Sepúlveda³

et al. 2021

Sensors

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show abstract

“…One other use of Isolation Forest was presented by the authors of [51], where a comparison was made between Isolation Forest, One-Class Support Vector Machine, and Elliptical Envelope, for a condition monitoring technique for wind turbine SCADA data. That paper examined a novel technique that compared two months of data, separated by a year, for several turbines that failed due to a fault in the gearbox.…”

Section: Isolation Forestmentioning

confidence: 99%

“…It is believed that these effects would not last for 5 consecutive 10-min periods. This technique is illustrated in Figure 3 and also used in [51]. The number of anomalies detected is less relevant than the proportion of anomalies per window.…”

Section: Condition Monitoring Techniquementioning

confidence: 99%

Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data

et al. 2021

Self Cite

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Wind turbine pitch system condition monitoring is an active area of research, and this paper investigates the use of the Isolation Forest Machine Learning model and Supervisory Control and Data Acquisition system data for this task. This paper examines two case studies, turbines with hydraulic or electric pitch systems, and uses an Isolation Forest to predict failure ahead of time. This novel technique compared several models per turbine, each trained on a different number of months of data. An anomaly proportion for three different time-series window lengths was compared, to observe trends and peaks before failure. The two cases were compared, and it was found that this technique could detect abnormal activity roughly 12 to 18 months before failure for both the hydraulic and electric pitch systems for all unhealthy turbines, and a trend upwards in anomalies could be found in the immediate run up to failure. These peaks in anomalous behaviour could indicate a future failure and this would allow for on-site maintenance to be scheduled. Therefore, this method could improve scheduling planned maintenance activity for pitch systems, regardless of the pitch system employed.

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“…Besides, some machine learning methods were applied to WT gearbox bearing condition monitoring 18 . McKinnon et al 19 presented support vector machine and isolation forest model to detect gearbox fault with SCADA data. Ruiming et al 20 combined with support vector regression (SVR), applied the corresponding relationship of the internal structure topology of WT and the observed SCADA data to detect faulty of the drive control system controller.…”

Section: Introductionmentioning

confidence: 99%

SSAE‐MLP: Stacked sparse autoencoders‐based multi‐layer perceptron for main bearing temperature prediction of large‐scale wind turbines

Xiao

Liu

et al. 2021

Concurrency and Computation

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Summary Condition monitoring and fault diagnosis of main bearings of large‐scale wind turbines is critical for improving its reliability and reducing operating and maintenance costs, especially in the early stages. To achieve the goal, this paper proposes a novel deep learning approach named stacked sparse autoencoder multi‐layer perceptron (SSAE‐MLP) with a new framework by utilizing supervisory control and data acquisition (SCADA) data for wind turbine main bearing temperature prediction. After the SCADA parameter variables related to the temperature change of the main bearing are extracted, the input characteristic vector is constructed. Then, the multiple sparse autoencoders are stacked to learn the deep features inside the input data by applying the greedy layerwise unsupervised learning algorithm. Finally, a regression predictor is added to the top layer of the stacked sparse autoencoder model for supervised learning to fine‐tune the overall network. Comparative experiments show that the proposed approach has superior performance for wind turbine main bearing temperature prediction.

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Comparison of New Anomaly Detection Technique for Wind Turbine Condition Monitoring Using Gearbox SCADA Data

Cited by 43 publications

References 29 publications

An Ensemble Learning Solution for Predictive Maintenance of Wind Turbines Main Bearing

An Ensemble Learning Solution for Predictive Maintenance of Wind Turbines Main Bearing

Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data

SSAE‐MLP: Stacked sparse autoencoders‐based multi‐layer perceptron for main bearing temperature prediction of large‐scale wind turbines

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