Condition Monitoring and Fault Diagnosis of Roller Element Bearing

Rolling element bearings play a crucial role in determining the overall health condition of a rotating machine. An effective condition-monitoring program on bearing operation can improve a machine’s operation efficiency, reduce the maintenance/replacement cost, and prolong the useful lifespan of a machine. This chapter presents a general overview of various condition-monitoring and fault diagnosis techniques for rolling element bearings in the current practice and discusses the pros and cons of each technique. The techniques introduced in the chapter include data acquisition techniques, major parameters used for bearing condition monitoring, signal analysis techniques, and bearing fault diagnosis techniques using either statistical features or artificial intelligent tools. Several case studies are also presented in the chapter to exemplify the application of these techniques in the data analysis as well as bearing fault diagnosis and pattern recognition

GAN Prior based Null-Space Learning for Consistent Super-Resolution

Consistency and realness have always been the two critical issues of image super-resolution. While the realness has been dramatically improved with the use of GAN prior, the state-of-the-art methods still suffer inconsistencies in local structures and colors (e.g., tooth and eyes). In this paper, we show that these inconsistencies can be analytically eliminated by learning only the null-space component while fixing the range-space part. Further, we design a pooling-based decomposition (PD), a universal range-null space decomposition for super-resolution tasks, which is concise, fast, and parameter-free. PD can be easily applied to state-of-the-art GAN Prior based SR methods to eliminate their inconsistencies, neither compromising the realness nor bringing extra parameters or computational costs. Besides, our ablation studies reveal that PD can replace pixel-wise losses for training and achieve better generalization performance when facing unseen downsamplings or even real-world degradation. Experiments show that the use of PD refreshes state-of-the-art SR performance and speeds up the convergence of training up to 2~10 times.Comment: Accepted by AAAI 202

DiffLLE: Diffusion-guided Domain Calibration for Unsupervised Low-light Image Enhancement

Existing unsupervised low-light image enhancement methods lack enough effectiveness and generalization in practical applications. We suppose this is because of the absence of explicit supervision and the inherent gap between real-world scenarios and the training data domain. In this paper, we develop Diffusion-based domain calibration to realize more robust and effective unsupervised Low-Light Enhancement, called DiffLLE. Since the diffusion model performs impressive denoising capability and has been trained on massive clean images, we adopt it to bridge the gap between the real low-light domain and training degradation domain, while providing efficient priors of real-world content for unsupervised models. Specifically, we adopt a naive unsupervised enhancement algorithm to realize preliminary restoration and design two zero-shot plug-and-play modules based on diffusion model to improve generalization and effectiveness. The Diffusion-guided Degradation Calibration (DDC) module narrows the gap between real-world and training low-light degradation through diffusion-based domain calibration and a lightness enhancement curve, which makes the enhancement model perform robustly even in sophisticated wild degradation. Due to the limited enhancement effect of the unsupervised model, we further develop the Fine-grained Target domain Distillation (FTD) module to find a more visual-friendly solution space. It exploits the priors of the pre-trained diffusion model to generate pseudo-references, which shrinks the preliminary restored results from a coarse normal-light domain to a finer high-quality clean field, addressing the lack of strong explicit supervision for unsupervised methods. Benefiting from these, our approach even outperforms some supervised methods by using only a simple unsupervised baseline. Extensive experiments demonstrate the superior effectiveness of the proposed DiffLLE

Genome analysis of Flaviramulus ichthyoenteri Th78T in the family Flavobacteriaceae: insights into its quorum quenching property and potential roles in fish intestine

Background: Intestinal microbes play significant roles in fish and can be possibly used as probiotics in aquaculture. In our previous study, Flaviramulus ichthyoenteri Th78(T), a novel species in the family Flavobacteriaceae, was isolated from fish intestine and showed strong quorum quenching (QQ) ability. To identify the QQ enzymes in Th78(T) and explore the potential roles of Th78(T) in fish intestine, we sequenced the genome of Th78(T) and performed extensive genomic analysis. Results: An N-acyl homoserine lactonase FiaL belonging to the metallo-beta-lactamase superfamily was identified and the QQ activity of heterologously expressed FiaL was confirmed in vitro. FiaL has relatively little similarity to the known lactonases (25.2 similar to 27.9% identity in amino acid sequence). Various digestive enzymes including alginate lyases and lipases can be produced by Th78(T), and enzymes essential for production of B vitamins such as biotin, riboflavin and folate are predicted. Genes encoding sialic acid lyases, sialidases, sulfatases and fucosidases, which contribute to utilization of mucus, are present in the genome. In addition, genes related to response to different stresses and gliding motility were also identified. Comparative genome analysis shows that Th78(T) has more specific genes involved in carbohydrate transport and metabolism compared to other two isolates in Flavobacteriaceae, both isolated from sediments. Conclusions: The genome of Th78(T) exhibits evident advantages for this bacterium to survive in the fish intestine, including production of QQ enzyme, utilization of various nutrients available in the intestine as well as the ability to produce digestive enzymes and vitamins, which also provides an application prospect of Th78(T) to be used as a probiotic in aquaculture

Unique bacterial communities and lifestyles in deep ocean blue holes: Insights from the Yongle Blue Hole (South China Sea)

Deep ocean blue holes possess steep physicochemical gradients, especially low dissolved oxygen concentration, which shape the extraordinary microbial communities. However, the environmental responses of microorganisms with different lifestyles and knowledge of culturable microorganisms in the blue holes are still unknown. Here, we investigated the bacterial community structure with different lifestyles of the world’s deepest blue hole - the Yongle Blue Hole (YBH) in the South China Sea using both culture-dependent and -independent methods. YBH can be divided by oxygen content into an oxic zone, a suboxic zone and two anoxic zones. The abundance of bacteria, archaea, genes dsrB and soxB were all higher in the free-living (FL) lifestyle than in the particle-associated (PA) lifestyle, yet the diversity and richness of PA bacteria were higher than that of FL bacteria. More Gammaproteobacteria and less Alphaproteobacteria, Chloroflexi and Nitrospinae were observed within the FL fraction than within the PA fraction. The relative abundance of sulfur-oxidizing bacteria (SOB) was dominant between 100-140 m (anoxic zone I) in YBH, with a maximum of 90.0% (140 m FL fraction). The SOB in YBH were mainly colorless sulfur bacteria and purple non-sulfur bacteria, of which Thiomicrorhabdus and Sulfurimonas were the main representatives. In addition, a total of 294 bacterial strains were isolated on a variety of media and culture conditions, and 22.2% (18/81) of anaerobic strains were identified as potential novel species. Our study reveals a distinction between FL and PA bacteria in YBH. It contributes to further understanding of the bacterial community in deep ocean blue holes, and provides bacterial resources for subsequent studies on their adaptation to extreme marine environments

Insights into the vertical stratification of microbial ecological roles across the deepest seawater column on Earth

The Earth’s oceans are a huge body of water with physicochemical properties and microbial community profiles that change with depth, which in turn influences their biogeochemical cycling potential. The differences between microbial communities and their functional potential in surface to hadopelagic water samples are only beginning to be explored. Here, we used metagenomics to investigate the microbial communities and their potential to drive biogeochemical cycling in seven different water layers down the vertical profile of the Challenger Deep (0–10,500 m) in the Mariana Trench, the deepest natural point in the Earth’s oceans. We recovered 726 metagenome-assembled genomes (MAGs) affiliated to 27 phyla. Overall, biodiversity increased in line with increased depth. In addition, the genome size of MAGs at ≥4000 m layers was slightly larger compared to those at 0–2000 m. As expected, surface waters were the main source of primary production, predominantly from Cyanobacteria. Intriguingly, microbes conducting an unusual form of nitrogen metabolism were identified in the deepest waters (>10,000 m), as demonstrated by an enrichment of genes encoding proteins involved in dissimilatory nitrate to ammonia conversion (DNRA), nitrogen fixation and urea transport. These likely facilitate the survival of ammonia-oxidizing archaea α lineage, which are typically present in environments with a high ammonia concentration. In addition, the microbial potential for oxidative phosphorylation and the glyoxylate shunt was enhanced in >10,000 m waters. This study provides novel insights into how microbial communities and their genetic potential for biogeochemical cycling differs through the Challenger deep water column, and into the unique adaptive lifestyle of microbes in the Earth’s deepest seawater