Wind Turbine Monitoring During Lightning Strikes
- kaggle.com
zipUpdated Sep 18, 2025
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EmailClick to copy linkLink copiedCiteNikita Manaenkov (2025). Wind Turbine Monitoring During Lightning Strikes [Dataset]. https://www.kaggle.com/datasets/nikitamanaenkov/wind-turbine-monitoring-during-lightning-strikeszip(6028695 bytes)Available download formatsDataset updatedSep 18, 2025AuthorsNikita ManaenkovLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionIntro
Lightning strikes pose a severe threat to wind turbine blades, often leading to cracks, delamination, or structural failure. Due to the difficulty of capturing real-time monitoring data during such rare events, comprehensive datasets are scarce. This dataset (MDWTBM-LS) provides a rare multimodal collection of sensor-based measurements for wind turbine blades subjected to lightning strikes.
Dataset Overview
The dataset contains real-time records of turbine blade conditions before, during, and after lightning strikes, including:
- Strain measurements (spanwise and chordwise directions)
- Vibration signals from fiber optic accelerometer sensors
- Load data from fiber optic load sensors
- Operational parameters: wind speed, wind power, rotor speed, temperature, pitch angle (angle of variable propeller), and deployment location
Sensors were installed at multiple sections of the blade (root, 1/3 length, 2/3 length) to capture dynamic responses. This multimodal dataset enables the development of machine learning models for fault detection, condition monitoring, and predictive maintenance of wind turbines under extreme environmental conditions.
Multimodal Signal Dataset for Fault Detection in PMSM-Driven Elevators Under...
- zenodo.org
Updated Jun 7, 2025ShareFacebookTwitterEmailClick to copy linkLink copiedCiteVasileios Vlachou; Vasileios Vlachou; THEOKLITOS KARAKATSANIS; THEOKLITOS KARAKATSANIS; Dimitrios Efstathiou; Dimitrios Efstathiou; Eftychios Vlachou; Stavros Vologiannidis; Stavros Vologiannidis; Vasiliki Balaska; Vasiliki Balaska; Antonios Gasteratos; Antonios Gasteratos; Eftychios Vlachou (2025). Multimodal Signal Dataset for Fault Detection in PMSM-Driven Elevators Under Real Operating Conditions [Dataset]. http://doi.org/10.5281/zenodo.15613954Unique identifierhttps://doi.org/10.5281/zenodo.15613954Dataset updatedJun 7, 2025AuthorsVasileios Vlachou; Vasileios Vlachou; THEOKLITOS KARAKATSANIS; THEOKLITOS KARAKATSANIS; Dimitrios Efstathiou; Dimitrios Efstathiou; Eftychios Vlachou; Stavros Vologiannidis; Stavros Vologiannidis; Vasiliki Balaska; Vasiliki Balaska; Antonios Gasteratos; Antonios Gasteratos; Eftychios VlachouLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionThis dataset contains synchronized time-series recordings of current and vibration signals collected from a fully operational residential elevator system equipped with a surface-mounted Permanent Magnet Synchronous Motor (PMSM). The data were acquired under healthy and faulty operating conditions, across various load levels and motion directions. The dataset supports research in predictive maintenance, signal analysis, and fault classification for PMSM-based elevator drives.
All data were collected non-intrusively using industrial sensors:
Hioki PW3390 power analyzer for current and THD,
Triaxial accelerometer (ISO 20816-3 compliant) for vibration.
The system is a 9-stop, 6-passenger MRL elevator, driven by a 5.1 kW, 12-pole PMSM motor.
No signal post-processing was applied; raw sensor values are preserved for reproducibility.
SOMAR SA - Vibration Sensor Measurement Data
- kaggle.com
zipUpdated Oct 27, 2025ShareFacebookTwitterEmailClick to copy linkLink copiedCiteLukasz Duss (2025). SOMAR SA - Vibration Sensor Measurement Data [Dataset]. https://www.kaggle.com/datasets/lukaszduss/somar-sa-vibration-sensors-timeserieszip(40670665 bytes)Available download formatsDataset updatedOct 27, 2025AuthorsLukasz DussLicensehttps://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
Descriptionhttps://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F28976641%2F8bd2ed22dad4307b7d3acab2f66264c3%2Flogo.png?generation=1761566519357344&alt=media" alt="">
--- EN ---
The files S74.csv and S76.csv contain raw measurement data from WS-VT1 vibration and temperature sensors manufactured by SOMAR SA.
The sensors operate within the Machine Diagnostics System SDM-1, developed as part of the project “Hardware–software system for machine and device diagnostics based on a wireless network of monitoring sensors and methods of knowledge engineering and computational intelligence”, co-funded by the European Regional Development Fund under agreement no. POIR.01.01.01-00-0304/19-00 dated 30 December 2019.
The sensors are installed on the industrial fan motors of gas furnaces used to preheat non-ferrous metal billets prior to plastic forming.
The data structure in the file is as follows:
[Date YYYY-MM-DD] [time]; [temperature, °C]; [maximum vibration acceleration, mg]; [RMS vibration acceleration, mg]
Example: 2024-11-07 07:00:18;18.3;55;45
--- PL ---
Pliki S74.csv i S76.csv zawierają surowe dane pomiarowe z czujników wibracji i temperatury WS-VT1 firmy SOMAR SA
Czujniki pracującą w Systemie Diagnostyki Maszyn SDM-1 rozwijanym w ramach projektu „Sprzętowo-programowy system diagnostyki maszyn i urządzeń bazujących na bezprzewodowej sieci czujników monitorujących oraz metodach inżynierii wiedzy i inteligencji obliczeniowej” współfinansowanego z Europejskiego Funduszu Rozwoju Regionalnego zgodnie z umową nr POIR.01.01.01-00-0304/19-00 z dnia 30.12.2019.
Czujniki zainstalowane na silnikach przemysłowych wentylatorów pieców gazowych służących do podgrzewania kęsów metali nieżelaznych przed obróbką plastyczną.
Struktura danych w pliku jest następująca:
[Data RRRR-MM-DD] [czas];[temperatura, C];[max. przyspieszenie drgań, mg];[wart. Skuteczna przyspieszenia drgań, mg]
Przykładowo: 2024-11-07 07:00:18;18.3;55;45
SOMAR SPÓŁKA AKCYJNA
ul. Karoliny 4,
40-186 Katowice, Polska
KRS: 0000407623
NIP: 6340196270
REGON: 003601740
System Diagnostyki Maszyn SDM-1 był rozwijany w ramach projektu „Sprzętowo-programowy system diagnostyki maszyn i urządzeń bazujących na bezprzewodowej sieci czujników monitorujących oraz metodach inżynierii wiedzy i inteligencji obliczeniowej” współfinansowanego z Europejskiego Funduszu Rozwoju Regionalnego zgodnie z umową nr POIR.01.01.01-00-0304/19-00 z dnia 30.12.2019.
https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F28976641%2F2e5d839202a7cbfb4bb9640694fd32a7%2FNCBR.png?generation=1763105574206842&alt=media" alt="">
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FacebookTwitterEmailWind Turbine Monitoring During Lightning Strikes
Strain, vibration, load, and operational data
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Intro
Lightning strikes pose a severe threat to wind turbine blades, often leading to cracks, delamination, or structural failure. Due to the difficulty of capturing real-time monitoring data during such rare events, comprehensive datasets are scarce. This dataset (MDWTBM-LS) provides a rare multimodal collection of sensor-based measurements for wind turbine blades subjected to lightning strikes.
Dataset Overview
The dataset contains real-time records of turbine blade conditions before, during, and after lightning strikes, including:
- Strain measurements (spanwise and chordwise directions)
- Vibration signals from fiber optic accelerometer sensors
- Load data from fiber optic load sensors
- Operational parameters: wind speed, wind power, rotor speed, temperature, pitch angle (angle of variable propeller), and deployment location
Sensors were installed at multiple sections of the blade (root, 1/3 length, 2/3 length) to capture dynamic responses. This multimodal dataset enables the development of machine learning models for fault detection, condition monitoring, and predictive maintenance of wind turbines under extreme environmental conditions.