IOT-Enabled Fault Diagnosis and Monitoring for Small Wind Turbine
Bibliographic record
Abstract
Electrical energy is the most dependable form of energy. The advancement of technology demands substantial energy use. Conventional energy sources are producing pollution, and fossil fuels are diminishing daily, so paving the way for renewable energy sources. Wind energy is the most reliant renewable energy source. The maintenance of wind turbines is intricate, continuous monitoring is challenging due to their elevated positions, and they are situated in rural locations. A dependable condition monitoring system is crucial for turbines working on wind. to reduce downtime and enhance output. The objective of this project is to monitor the parameters of turbine working on wind and enhance early defect identification. Sensors are employed to assess the state of the wind turbine. The utilized sensors are a temperature sensor, a vibration sensor, and a voltage sensor. Should any sensor provide an anomalous value, the data is transmitted to the IoT cloud within a matter of seconds. This project utilizes an Arduino UNO and a Wi-Fi module. The Arduino UNO gathers sensor data from several wind turbine sensors, and the Wi-Fi module transmits this information to an IoT cloud application, such as Telegram, already loaded on our mobile devices. The operation of the kit and the performance evaluation have been conducted on the suggested system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".