Increasing Wind Turbine Drivetrain Bearing Vibration Monitoring Detectability Using an Artificial Neural Network Implementation
The highest costs due to premature failures in wind turbine drivetrains are related to defects in the gearbox, with bearing failures being overrepresented. Vibration monitoring has been identified as the primary tool to detect and diagnose these types of failures. However, late or no signs of the fa...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-04-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/8/3588 |
| _version_ | 1797537466338508800 |
|---|---|
| author | Daniel Strömbergsson Pär Marklund Kim Berglund |
| author_facet | Daniel Strömbergsson Pär Marklund Kim Berglund |
| author_sort | Daniel Strömbergsson |
| collection | DOAJ |
| description | The highest costs due to premature failures in wind turbine drivetrains are related to defects in the gearbox, with bearing failures being overrepresented. Vibration monitoring has been identified as the primary tool to detect and diagnose these types of failures. However, late or no signs of the failures are still being reported. Artificial neural networks (ANNs) has been shown to favourably be used as a classifier of bearing failures to increase the detection and diagnosis performance, which requires labelled data when training for all types of considered failures. However, less work has been done with an ANN used to create descriptive functions of the vibration and turbine operation data relationship and thereby negating inherent variance in the vibration data and increasing the detectability when a defect appears. Therefore, this study utilizes the relationship between the rotational speed recorded during a vibration measurement and the calculated condition indicator values of specific bearing failures in three wind turbine gearbox failures. An ANN establishes a function between the rotational speed and condition indicator values with healthy training data collected before the failure occurred. Thereafter, whole datasets leading up to the changing of the gearboxes is used to predict the condition indicator values without the failure influence. The difference between the predicted and true values show an increased sensitivity of the detection in two cases of gearbox output shaft bearing failures as well as indicating a planet bearing failure which with the previous data had gone undetected. |
| first_indexed | 2024-03-10T12:15:30Z |
| format | Article |
| id | doaj.art-856fcca426474c1b9c12c3fb24714589 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T12:15:30Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-856fcca426474c1b9c12c3fb247145892023-11-21T15:51:45ZengMDPI AGApplied Sciences2076-34172021-04-01118358810.3390/app11083588Increasing Wind Turbine Drivetrain Bearing Vibration Monitoring Detectability Using an Artificial Neural Network ImplementationDaniel Strömbergsson0Pär Marklund1Kim Berglund2Division of Machine Elements, Department of Engineering Sciences and Mathematics, Luleå University of Technology, SE 97187 Luleå, SwedenDivision of Machine Elements, Department of Engineering Sciences and Mathematics, Luleå University of Technology, SE 97187 Luleå, SwedenDivision of Machine Elements, Department of Engineering Sciences and Mathematics, Luleå University of Technology, SE 97187 Luleå, SwedenThe highest costs due to premature failures in wind turbine drivetrains are related to defects in the gearbox, with bearing failures being overrepresented. Vibration monitoring has been identified as the primary tool to detect and diagnose these types of failures. However, late or no signs of the failures are still being reported. Artificial neural networks (ANNs) has been shown to favourably be used as a classifier of bearing failures to increase the detection and diagnosis performance, which requires labelled data when training for all types of considered failures. However, less work has been done with an ANN used to create descriptive functions of the vibration and turbine operation data relationship and thereby negating inherent variance in the vibration data and increasing the detectability when a defect appears. Therefore, this study utilizes the relationship between the rotational speed recorded during a vibration measurement and the calculated condition indicator values of specific bearing failures in three wind turbine gearbox failures. An ANN establishes a function between the rotational speed and condition indicator values with healthy training data collected before the failure occurred. Thereafter, whole datasets leading up to the changing of the gearboxes is used to predict the condition indicator values without the failure influence. The difference between the predicted and true values show an increased sensitivity of the detection in two cases of gearbox output shaft bearing failures as well as indicating a planet bearing failure which with the previous data had gone undetected.https://www.mdpi.com/2076-3417/11/8/3588vibration measurementsbearing failurewind turbine drivetrain bearingsartificial neural networks |
| spellingShingle | Daniel Strömbergsson Pär Marklund Kim Berglund Increasing Wind Turbine Drivetrain Bearing Vibration Monitoring Detectability Using an Artificial Neural Network Implementation Applied Sciences vibration measurements bearing failure wind turbine drivetrain bearings artificial neural networks |
| title | Increasing Wind Turbine Drivetrain Bearing Vibration Monitoring Detectability Using an Artificial Neural Network Implementation |
| title_full | Increasing Wind Turbine Drivetrain Bearing Vibration Monitoring Detectability Using an Artificial Neural Network Implementation |
| title_fullStr | Increasing Wind Turbine Drivetrain Bearing Vibration Monitoring Detectability Using an Artificial Neural Network Implementation |
| title_full_unstemmed | Increasing Wind Turbine Drivetrain Bearing Vibration Monitoring Detectability Using an Artificial Neural Network Implementation |
| title_short | Increasing Wind Turbine Drivetrain Bearing Vibration Monitoring Detectability Using an Artificial Neural Network Implementation |
| title_sort | increasing wind turbine drivetrain bearing vibration monitoring detectability using an artificial neural network implementation |
| topic | vibration measurements bearing failure wind turbine drivetrain bearings artificial neural networks |
| url | https://www.mdpi.com/2076-3417/11/8/3588 |
| work_keys_str_mv | AT danielstrombergsson increasingwindturbinedrivetrainbearingvibrationmonitoringdetectabilityusinganartificialneuralnetworkimplementation AT parmarklund increasingwindturbinedrivetrainbearingvibrationmonitoringdetectabilityusinganartificialneuralnetworkimplementation AT kimberglund increasingwindturbinedrivetrainbearingvibrationmonitoringdetectabilityusinganartificialneuralnetworkimplementation |