Szczegóły publikacji
Opis bibliograficzny
Boosted convolutional neural network algorithm for the classification of the bearing fault form 1-D raw sensor data / Paweł KNAP, Krzysztof LALIK, Patryk BAŁAZY // Sensors [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1424-8220. — 2023 — vol. 23 iss. 9 art. no. 4295, s. 1–22. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 20–22, Abstr. — Publikacja dostępna online od: 2023-04-26
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 147433 |
|---|---|
| Data dodania do BaDAP | 2023-06-27 |
| Tekst źródłowy | URL |
| DOI | 10.3390/s23094295 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Sensors |
Abstract
Renewable energy sources are a growing branch of industry. One such source is wind farms, which have significantly increased their number over recent years. Alongside the increased number of turbines, maintenance problems are growing. There is a need for newer and less intrusive predictive maintenance methods. About 40% of all turbine failures are due to bearing failure. This paper presents a modified neural direct classifier method using raw accelerometer measurements as input. This proprietary platform allows for better damage prediction results than convolutional networks in vibration spectrum image analysis. It operates in real time and without signal processing methods converting the signal to a time–frequency spectrogram. Image processing methods can extract features from a set of preset features and based on their importance. The proposed method is not based on feature extraction from image data but on automatically finding a set of features from raw tabular data. This fact significantly reduces the computational cost of detection and improves the failure detection accuracy compared to the classical methods. The model achieved a precision of 99.32% on the validation set, and 96.3% during bench testing. These results were an improvement over the method that classifies time–frequency spectrograms of 97.76% for the validation set and 90.8% for the real-world tests, respectively.
