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- W4387192718 abstract "Rolling bearings have a wide range of applications in manufacturing fields such as aviation, aerospace, shipbuilding, and automobiles. Due to the limited lifespan of bearings, their failure can impact the operation of the entire device. Therefore, high-performance diagnostic methods are of great significance for utilizing rolling bearings. Compared with traditional diagnostic techniques, data-driven deep learning methods require more data for classification, and the effectiveness of data preprocessing will deeply affect the diagnostic results. In this paper, fast Fourier transform (FFT) operations are applied to the raw time-series data, and different high-dimensional datasets are constructed by combining various parameters in the time domain and frequency domain. Then, these datasets are fed into long short-term memory (LSTM) networks for fault classification. Simulation experiments were conducted on popular bearing data sets with different constructions to find an accurate and efficient fault diagnosis method. The experimental results can provide a reference for related research." @default.
- W4387192718 created "2023-09-30" @default.
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- W4387192718 date "2023-07-21" @default.
- W4387192718 modified "2023-10-01" @default.
- W4387192718 title "A Comprehensive Study of Fast Fourier Transform for Bearing Fault Diagnosis with Long Short-Term Memory Networks" @default.
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- W4387192718 doi "https://doi.org/10.1109/iceemt59522.2023.10263172" @default.
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