Zephyr: a Data-Centric Framework for Predictive Maintenance of Wind Turbines
Because wind turbines often operate through harsh weather events, under variable operating conditions, and in difficult-to-access locations, turbine maintenance is often challenging and costly. In this thesis, we present Zephyr, a flexible machine learning framework for predictive maintenance of win...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2023
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| Online Access: | https://hdl.handle.net/1721.1/150202 |
| _version_ | 1826205522210062336 |
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| author | Hartwell, Frances R. |
| author2 | Veeramachaneni, Kalyan |
| author_facet | Veeramachaneni, Kalyan Hartwell, Frances R. |
| author_sort | Hartwell, Frances R. |
| collection | MIT |
| description | Because wind turbines often operate through harsh weather events, under variable operating conditions, and in difficult-to-access locations, turbine maintenance is often challenging and costly. In this thesis, we present Zephyr, a flexible machine learning framework for predictive maintenance of wind energy assets. Manual analysis of wind turbine data is difficult and time-consuming due to its volume, variety, and, most importantly, the need for quick detection of issues. Machine learning (ML) methods are able to automate large-scale data analysis. However, the enormous amount of contextual information required to actually understand the data impedes the ability of ML frameworks to provide actionable insights. To this end, Zephyr enables Subject Matter Experts (SMEs) to incorporate their knowledge at various stages of ML model development. The Zephyr framework consists of a signal-processing-based featurization library, a data labeling algorithm – which helps analyze operational data and maintenance events in order to create labels for machine learning problems – and a set of automated machine learning pipelines for predicting outcome types. SMEs incorporate their expertise by providing labeling functions, bands for frequency domain-based featurization, and several other inputs in an intuitive way. We demonstrate the efficacy of this framework through two case studies involving maintenance operation data from wind turbines. Moreover, we show that ML performance can increase when involving domain expertise by a value as high as 48%. |
| first_indexed | 2024-09-23T13:14:23Z |
| format | Thesis |
| id | mit-1721.1/150202 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T13:14:23Z |
| publishDate | 2023 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1502022023-04-01T03:29:18Z Zephyr: a Data-Centric Framework for Predictive Maintenance of Wind Turbines Hartwell, Frances R. Veeramachaneni, Kalyan Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Because wind turbines often operate through harsh weather events, under variable operating conditions, and in difficult-to-access locations, turbine maintenance is often challenging and costly. In this thesis, we present Zephyr, a flexible machine learning framework for predictive maintenance of wind energy assets. Manual analysis of wind turbine data is difficult and time-consuming due to its volume, variety, and, most importantly, the need for quick detection of issues. Machine learning (ML) methods are able to automate large-scale data analysis. However, the enormous amount of contextual information required to actually understand the data impedes the ability of ML frameworks to provide actionable insights. To this end, Zephyr enables Subject Matter Experts (SMEs) to incorporate their knowledge at various stages of ML model development. The Zephyr framework consists of a signal-processing-based featurization library, a data labeling algorithm – which helps analyze operational data and maintenance events in order to create labels for machine learning problems – and a set of automated machine learning pipelines for predicting outcome types. SMEs incorporate their expertise by providing labeling functions, bands for frequency domain-based featurization, and several other inputs in an intuitive way. We demonstrate the efficacy of this framework through two case studies involving maintenance operation data from wind turbines. Moreover, we show that ML performance can increase when involving domain expertise by a value as high as 48%. M.Eng. 2023-03-31T14:39:18Z 2023-03-31T14:39:18Z 2023-02 2023-02-27T18:43:26.147Z Thesis https://hdl.handle.net/1721.1/150202 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Hartwell, Frances R. Zephyr: a Data-Centric Framework for Predictive Maintenance of Wind Turbines |
| title | Zephyr: a Data-Centric Framework for Predictive Maintenance of Wind Turbines |
| title_full | Zephyr: a Data-Centric Framework for Predictive Maintenance of Wind Turbines |
| title_fullStr | Zephyr: a Data-Centric Framework for Predictive Maintenance of Wind Turbines |
| title_full_unstemmed | Zephyr: a Data-Centric Framework for Predictive Maintenance of Wind Turbines |
| title_short | Zephyr: a Data-Centric Framework for Predictive Maintenance of Wind Turbines |
| title_sort | zephyr a data centric framework for predictive maintenance of wind turbines |
| url | https://hdl.handle.net/1721.1/150202 |
| work_keys_str_mv | AT hartwellfrancesr zephyradatacentricframeworkforpredictivemaintenanceofwindturbines |