The Role Of AI In Identifying Bearing Faults Of Renewable Energy Systems

The increasing complexity and power requirements of communication systems make them impractical for widespread usage. Theoretical systems have been superbly applied to the actual world by artificial intelligence (AI), with less complexity producing greater outcomes. Here, they contrast two kinds of detectors—one based on deep learning and the other on the more conventional maximum likelihood (ML) decoder—and two methods for multiuser Visible Light Communication (VLC) systems: Successive Interference Cancellation (SIC) and Non-Orthogonal Multiple Access (NOMA). Examine the differences and similarities between Index Modulation (IM), Orthogonal Frequency Division Multiplexing (OFDM), and the new Orbital Angular Momentum (OAM) multiplexing. Simulated results for a variety of signal-to-noise ratios (SNRs) reveal that, for both users, deep learning-based structures provide superior decryption at the receiver end, mainly for advanced SNR values, when compared to ML-based systems. By using deep learning, the detection error may be reduced to about 20% for low SNR values and roughly 30% for high SNR values.