Vibration Signal Noise-Reduction Method of Slewing Bearings Based on the Hybrid Reinforcement Chameleon Swarm Algorithm, Variate Mode Decomposition, and Wavelet Threshold (HRCSA-VMD-WT) Integrated Model.

To enhance fault detection in slewing bearing vibration signals, an advanced noise-reduction model, HRCSA-VMD-WT, is designed for effective signal noise elimination. This model innovates by refining the Chameleon Swarm Algorithm (CSA) into a more potent Hybrid Reinforcement CSA (HRCSA), incorporating strategies from Chaotic Reverse Learning (CRL), the Whale Optimization Algorithm's (WOA) bubble-net hunting, and the greedy strategy with the Cauchy mutation to diversify the initial population, accelerate convergence, and prevent local optimum entrapment. Furthermore, by optimizing Variate Mode Decomposition (VMD) input parameters with HRCSA, Intrinsic Mode Function (IMF) components are extracted and categorized into noisy and pure signals using cosine similarity. Subsequently, the Wavelet Threshold (WT) denoising targets the noisy IMFs before reconstructing the vibration signal from purified IMFs, achieving significant noise reduction. Comparative experiments demonstrate HRCSA's superiority over Particle Swarm Optimization (PSO), WOA, and Gray Wolf Optimization (GWO) regarding convergence speed and precision. Notably, HRCSA-VMD-WT increases the Signal-to-Noise Ratio (SNR) by a minimum of 74.9% and reduces the Root Mean Square Error (RMSE) by at least 41.2% when compared to both CSA-VMD-WT and Empirical Mode Decomposition with Wavelet Transform (EMD-WT). This study improves fault detection accuracy and efficiency in vibration signals and offers a dependable and effective diagnostic solution for slewing bearing maintenance.

Time- and compound-dependent microbial community compositions and oil hydrocarbon degrading activities in seawater near the Chinese Zhoushan Archipelago.

Marine microorganisms play an irreplaceable role in removing spilled oil. Zhoushan archipelago has one of the busiest ports and oil stockpiles in China. However, little is known about which and how fast oil-degrading microorganisms could biodegrade spilled oil here. By combining 14C-/3H-based radiotracer assays and MiSeq sequencing, we report the successive pattern of microbial oil-degrading activities and community compositions. The biodegradation rates of alkanes and PAHs (Polycyclic Aromatic Hydrocarbons) were significantly stimulated by oil addition, and reached their maximum after incubation for 3 and 7 days, respectively. Meanwhile, the abundances of alkB and phnAc genes increased and the bacterial communities continuously shifted. Potential oil-degrading bacteria Alcanivorax, Erythrobacter were the dominant degraders by day 3, whereas the dominant degraders shifted to C1-B045, Alteromonas, Pseudohongiella in the later period. These results provide valuable insights into the cooperative system of the versatile oil-degrading bacteria in successively biodegrading complex oil hydrocarbons in oil spills.