Diagnostic Models for Wind Turbine Gearbox Components Using SCADA Time Series Data

Orozco, Rafael; Sheng, Shuangwen; Phillips, Caleb

doi:10.1109/icphm.2018.8448545

Cited by 30 publications

(16 citation statements)

References 7 publications

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“…13(b) with a fault threshold of ±0.25 • C. Although it is observed that some points are out of control at the beginning of the control chart from March 10, 2013, through to January 12, 2014, we do not have enough elements to validate if this event was fault-driven. But looking at the event towards the end of the control chart, we see numerous points outside the fault threshold starting from April 3, 2016, through to December 25,2016. Before this extended dramatic event, we observed an out-of-control point on May 10, 2015.…”

Section: Wind Turbine R80736mentioning

confidence: 98%

“…The method was efficient to detect overtemperature in the high-speed side of the gearbox bearing. The research [25] proposed a model that detects abnormal spikes in wind turbine components by adjusting temperature data for effects caused by ambient temperature and when the turbine is outputting power. Regression models with inputs variables (power output and ambient temperature) and output variable (component temperature) were built.…”

Section: A Regression-based Anomaly Detectionmentioning

confidence: 99%

“…The fault threshold is defined by two control limits used to evaluate abnormal behaviours: Upper Control Limit (UCL) Lower Control Limit (LCL) The two control limits describe the sensitivity of the control chart, which is expressed as multiples of σ of the deviation's distribution. Where σ is standard deviation calculated from the moving range M R, which is the difference between the i-th deviation and the last one, ( 22) to (25) shows the process of computing the control chart sensitivity:…”

Section: Post-processingmentioning

confidence: 99%

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Data-Driven Predictive Maintenance of Wind Turbine Based on SCADA Data

Udo

Muhammad²

2021

IEEE Access

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Rystad Energy's analysis shows that installed offshore wind capacity will rise to 27.5 GW in 2026 from 10.5 GW in 2020. This report indicates that increasingly complex maintenance needs must be met for wind turbines(WTs). IRENA report shows that offshore wind operation and maintenance (OM) costs typically constitute 16-25% of the cost of electricity for offshore wind farms deployed in the G20 countries. Data collection and analytics, predictive maintenance, and production output optimisation of WTs must be explored to increase operational reliability and reduce maintenance costs of WTs. Predictive maintenance in wind turbines can be achieved by analysing data obtained by sensors already equipped with the WT. This network of sensors forms part of a Supervisory Control and Data Acquisition (SCADA) system. We developed a method for monitoring and detecting anomalies in the WT critical components, such as the gearbox and the generator. The proposed approach is based on the historical SCADA data that is common in most wind farms. We developed models using extreme gradient boosting (XGBoost) and Long Short-Term Memory (LSTM) to build the characteristics behaviour of critical WT components, and Statistical Process Control (SPC) was used to evaluate its anomalous behaviour. The proposed method was tested on two real case studies regarding six different WT to determine its effectiveness and applicability.

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Section: Wind Turbine R80736mentioning

confidence: 98%

Section: A Regression-based Anomaly Detectionmentioning

confidence: 99%

Section: Post-processingmentioning

confidence: 99%

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Data-Driven Predictive Maintenance of Wind Turbine Based on SCADA Data

Udo

Muhammad²

2021

IEEE Access

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“…Iyengar et al (112) use a graphical models approach to examine correlations between the power outputs of nearby residential solar panels, in order to flag potential anomalies. In the context of wind power, Orozco et al (113) identify wind turbine failures in historical data by building supervised models to predict gearbox component temperatures, and then analyzing the residuals of these models to identify anomalous temperatures. In the context of nuclear power plants, Calivá et al (114) propose a method based on supervised learning, clustering, and denoising methods to detect anomalies in simulated nuclear reactor data.…”

Section: Predictive Maintenance and Fault Detectionmentioning

confidence: 99%

Machine Learning for Sustainable Energy Systems

Donti

Kolter

2021

Annu. Rev. Environ. Resour.

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In recent years, machine learning has proven to be a powerful tool for deriving insights from data. In this review, we describe ways in which machine learning has been leveraged to facilitate the development and operation of sustainable energy systems. We first provide a taxonomy of machine learning paradigms and techniques, along with a discussion of their strengths and limitations. We then provide an overview of existing research using machine learning for sustainable energy production, delivery, and storage. Finally, we identify gaps in this literature, propose future research directions, and discuss important considerations for deployment. Expected final online publication date for the Annual Review of Environment and Resources, Volume 46 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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“…Recently, monitoring of gearbox oil debris through analysis of the filter elements was also completed (Sheng and Roberts 2017). As the industry has started recognizing the benefits of condition monitoring and prognostics, related R&D has been launched in the areas of supervisory control and data acquisition (SCADA)-based modeling for gearbox fault detection and prediction (Orozco, Sheng, and Phillips 2018;Williams et al 2020;Desai et al 2020), and physics domain modeling with inputs from the data domain for component RUL prediction . A prognostics and health management (PHM) framework for wind turbines was also introduced, which highlighted RUL as the expected output and described the integration of data and physics domain modeling methods (Sheng and Guo 2019).…”

Section: Improving Wind Turbine Availability Through Data Analyticsmentioning

confidence: 99%