Flywheels are one of the promising energy storage devices for stabilizing the power quality of reusable energy, owing to their fast response time and high cycle lifetime. However, it can be catastrophic when they fail, because they store kinetic energy that can be released in a short amount of time. Data-driven monitoring techniques have been proposed to solve fault detection tasks in several types of rotation machinery with ball bearings. In contrast to traditional approaches using human-engineered features that require a high level of expertise, a data-driven approach requires no such prior knowledge. However, flywheels differ from typical rotation machinery because they use a magnetic or pivot bearing, and it is unclear whether a data-driven method can be used to detect a fault. In the present study, the effectiveness of a data-driven fault detection system for flywheels that use pivot bearings is evaluated. A flywheel fault progresses in several stages, and vibration data were collected for a flywheel running at each of those stages. A convolutional neural network (CNN) was exploited to detect a fault of the flywheel and identify a mode of the fault. Experimental comparisons conducted using vibration signals from an actual flywheel demonstrated that faulty operational state observed at an end of the flywheel's life can be detected with high accuracy using a data-driven method.
|ジャーナル||Procedia Structural Integrity|
|出版ステータス||Published - 2019|
|イベント||3rd International Conference on Structural Integrity, ICSI 2019 - Funchal, Madeira, Portugal|
継続期間: 2019 9月 2 → 2019 9月 5
ASJC Scopus subject areas